text:
important form of artistic expression in Taiwan. Dinner of Luci\'{e}rnaga is an interdisciplinary project produced by more than ten talented members which include the director, dancer, choreographer, artist, interactive designer, sound designer, iPhone app engineer, image processing designer, stage designer and light designer. The goal of this project is to create new modes of interactive participation between the performers and audience through the use of an innovative iPhone application that links dancer to audience and audience to dancer. The application not only plays a key role in connecting the audience and dancer, but also uses an interesting sound generation application that enhances the spectators' experience. It creates and shares special interactive experiences. Dinner of Luci\'{e}rnaga is a story about the relationship of light and human in the digital era. It is a stunning performance in visuals and interactive process with focus on new interface that are put into use in authentic environments for validation by audience. In this paper, we will discuss our artistic motivation, the development of the digitally interactive performances, and our process for creating the digitally interactive performance entitled Dinner of Luci\'{e}rnaga.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {559–568},
numpages = {10},
keywords = {digital performance, image processing, mobile art, iphone app, interaction},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2393427,
author = {Yang, Xin and Cheng, Kwang-Ting (Tim)},
title = {Accelerating SURF Detector on Mobile Devices},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2393427},
doi = {10.1145/2393347.2393427},
abstract = {Running a SURF (Speeded Up Robust Features) detector on mobile devices remains too slow to support emerging applications such as mobile augmented reality. Porting it without adapting the algorithm to account for mobile platform limitations could result in significant runtime degradation. In this paper, we identify two mismatches between the SURF algorithm and the mobile hardware that cause substantial slow-down of the point detection process: 1) mismatch between the data access pattern and the small cache size, and 2) mismatch between the huge amount of branches and high pipeline hazard penalty. To address the mismatches, we propose two techniques: tiled SURF and gradient moment based orientation assignment. Tiled SURF improves data locality and greatly reduces memory traffic. A method for determining the optimal tile sizes, named content-aware tiling, is designed to minimize runtime and maximize detection accuracy. To avoid the penalties caused by pipeline hazards, we replace the original orientation operator with branching-free gradient moment computations. The proposed techniques are tested on three mobile platforms. Comparing to the original SURF, the accelerated SURF achieves a 6x~8x speedup without sacrificing recognition accuracy. Meanwhile, it achieves 59%~80% reductions in the runtime ratio of the detector running on mobile platforms compared with on x86-based PCs.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {569–578},
numpages = {10},
keywords = {acceleration, surf, pipeline hazards penalty, branch prediction, data access patterns, point detection, cache miss, mobile phones},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2393428,
author = {Dai, Lican and Yue, Huanjing and Sun, Xiaoyan and Wu, Feng},
title = {IMShare: Instantly Sharing Your Mobile Landmark Images by Search-Based Reconstruction},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2393428},
doi = {10.1145/2393347.2393428},
abstract = {Instantly sharing captured landmark images is becoming fashionable, much like when you write a blog or chat with friends by mobile phone. However, real-time transmission of high-resolution images poses a significant challenge to contemporary mobile networks. Either long delays in transmission or largely reduced image resolution can lead to bad user experience. In this paper, we propose a novel mobile-cloud scheme IMShare to enable instant sharing of high-resolution images. On the mobile side, high-resolution images are described by their thumbnails and SIFT (Scale-Invariant Feature Transform) descriptors. After compression, data sent by mobile phones can be reduced to an average of 2.6 kilobytes (KB) per mega pixel. On the cloud side, high-resolution images are reproduced from a large-scale image database by retrieving partial duplicate images by SIFT descriptors and stitching corresponding image patches together under the guidance of the thumbnails. IMShare is the first scheme to demonstrate that not only visually pleasant images can be reconstructed using this mobile-cloud method but also the reconstruction can be done in seconds using parallel computing. Our user study of a half million images in a database shows that the proposed IMShare significantly outperforms the current method on subjective quality.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {579–588},
numpages = {10},
keywords = {image reconstruction, image sharing, sift descriptor, partial duplicate image retrieval},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2393429,
author = {Liu, Jiajun and Huang, Zi and Chen, Lei and Shen, Heng Tao and Yan, Zhixian},
title = {Discovering Areas of Interest with Geo-Tagged Images and Check-Ins},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2393429},
doi = {10.1145/2393347.2393429},
abstract = {Geo-tagged image is an ideal source for the discovery of popular travel places. However, the aspects of popular venues for daily-life purposes like dining and shopping are often missing in the mined locations from geo-tagged images. Fortunately check-in websites provide us a unique opportunity of analyzing people's preferences in their daily lives to complement the knowledge mined from geo-tagged images. This paper presents a novel approach for the discovery of Areas of Interest (AoI). By analyzing both geo-tagged images and check-ins, the approach exploits travelers' flavors as well as the preferences of daily-life activities of local residents to find AoI in a city. The proposed approach consists of two major steps. Firstly, we devise a density-based clustering method to discover AoI, mainly based on the image densities but also reinforced by the secondary densities from the images' neighboring venues. Then we propose a novel joint authority analysis framework to rank AoI. The framework simultaneously considers both the location-location transitions, and the user-location relations. An interactive presentation interface for visualizing AoI is also presented. The approach is tested with very large datasets for Shanghai city. They consist of 49,460 geo-tagged images from Panoramio.com, and 1,361,547 check-ins from the check-in website Qieke.com. By evaluating the ranking accuracy and quality of AoI, we demonstrate great improvements of our method over compared methods.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {589–598},
numpages = {10},
keywords = {check-in, area of interest, geo-tagged photo},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/3246408,
author = {Hsu, Winston},
title = {Session Details: Full Paper Session 15: Image Content Analysis},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3246408},
doi = {10.1145/3246408},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2393431,
author = {Letessier, Pierre and Buisson, Olivier and Joly, Alexis},
title = {Scalable Mining of Small Visual Objects},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2393431},
doi = {10.1145/2393347.2393431},
abstract = {This paper presents a scalable method for automatically discovering frequent visual objects in large multimedia collections even if their size is very small. It first formally revisits the problem of mining or discovering such objects, and then generalizes two kinds of existing methods for probing candidate object seeds: weighted adaptive sampling and hashing-based methods. The idea is that the collision frequencies obtained with hashing-based methods can actually be converted into a prior probability density function given as input to a weighted adaptive sampling algorithm. This allows for an evaluation of any hashing scheme effectiveness in a more generalized way, and a comparison with other priors, e.g. guided by visual saliency concerns. We then introduce a new hashing strategy, working first at the visual level, and then at the geometric level. This strategy allows us to integrate weak geometric constraints into the hashing phase itself and not only neighborhood constraints as in previous works. Experiments conducted on a new dataset introduced in this paper will show that using this new hashing-based prior allows a drastic reduction of the number of tentative probes required to discover small objects instantiated several times in a large dataset.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {599–608},
numpages = {10},
keywords = {RMMH, LSH, scalable, small objects, discovery, computer vision, weak geometry, mining, hashing},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2393432,
author = {Zhang, Wei and Pang, Lei and Ngo, Chong-Wah},
title = {Snap-and-Ask: Answering Multimodal Question by Naming Visual Instance},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2393432},
doi = {10.1145/2393347.2393432},
abstract = {In real-life, it is easier to provide a visual cue when asking a question about a possibly unfamiliar topic, for example, asking the question, "Where was this crop circle found?". Providing an image of the instance is far more convenient than texting a verbose description of the visual properties, especially when the name of the query instance is not known. Nevertheless, having to identify the visual instance before processing the question and eventually returning the answer makes multimodal question-answering technically challenging. This paper addresses the problem of visual-to-text naming through the paradigm of answering-by-search in a two-stage computational framework, which is composed out of instance search (IS) and similar question ranking (QR). In IS, names of the instances are inferred from similar visual examples searched through a million-scale image dataset. For recalling instances of non-planar and non-rigid shapes, spatial configurations that emphasize topology consistency while allowing for local variations in matches have been incorporated. In QR, the candidate names of the instance are statistically identified from search results and directly utilized to retrieve similar questions from community-contributed QA (cQA) archives. By parsing questions into syntactic trees, a fuzzy matching between the inquirer's question and cQA questions is performed to locate answers and recommend related questions to the inquirer. The proposed framework is evaluated on a wide range of visual instances (e.g., fashion, art, food, pet, logo, and landmark) over various QA categories (e.g., factoid, definition, how-to, and opinion).},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {609–618},
numpages = {10},
keywords = {multimedia question answering, similar question search, visual instance search},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2393433,
author = {Liu, Si and Feng, Jiashi and Song, Zheng and Zhang, Tianzhu and Lu, Hanqing and Xu, Changsheng and Yan, Shuicheng},
title = {Hi, Magic Closet, Tell Me What to Wear!},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2393433},
doi = {10.1145/2393347.2393433},
abstract = {In this paper, we aim at a practical system, magic closet, for automatic occasion-oriented clothing recommendation. Given a user-input occasion, e.g., wedding, shopping or dating, magic closet intelligently suggests the most suitable clothing from the user's own clothing photo album, or automatically pairs the user-specified reference clothing (upper-body or lower-body) with the most suitable one from online shops.Two key criteria are explicitly considered for the magic closet system. One criterion is to wear properly, e.g., compared to suit pants, it is more decent to wear a cocktail dress for a banquet occasion. The other criterion is to wear aesthetically, e.g., a red T-shirt matches better white pants than green pants. To narrow the semantic gap between the low-level features of clothing and the high-level occasion categories, we adopt middle-level clothing attributes (e.g., clothing category, color, pattern) as a bridge. More specifically, the clothing attributes are treated as latent variables in our proposed latent Support Vector Machine (SVM) based recommendation model. The wearing properly criterion is described in the model through a feature-occasion potential and an attribute-occasion potential, while the wearing aesthetically criterion is expressed by an attribute-attribute potential. To learn a generalize-well model and comprehensively evaluate it, we collect a large clothing What-to-Wear (WoW) dataset, and thoroughly annotate the whole dataset with 7 multi-value clothing attributes and 10 occasion categories via Amazon Mechanic Turk. Extensive experiments on the WoW dataset demonstrate the effectiveness of the magic closet system for both occasion-oriented clothing recommendation and pairing.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {619–628},
numpages = {10},
keywords = {latent svm, clothing pairing, clothing recommendation},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2393434,
author = {Lu, Chun-Shien and Hsu, Chao-Yung},
title = {Constraint-Optimized Keypoint Inhibition/Insertion Attack: Security Threat to Scale-Space Image Feature Extraction},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2393434},
doi = {10.1145/2393347.2393434},
abstract = {Scale-space image feature extraction (SSIFE) has been widely adopted in broad areas due to its powerful resilience to attacks. However, the security threat to SSIFE-based applications, which will be addressed in this paper, is relatively unexplored.The security threat to SSIFT (called ST-SSIFE), composed of a constrained-optimization keypoint inhibition attack (KIHA) and a keypoint insertion attack (KISA), is specifically designed in this paper for scale-space feature extraction methods, such as SIFT and SURF.In ST-SSIFE, KIHA aims at making a fool of feature extraction protocols in that the detection rules are purposely violated so as to suppress the existence of a local maximum around a local region.We show that KIHA can be accomplished quickly via Lagrange multiplier but the resultant new keypoint generation (NKG) problem can be solved via Karush Kuhn Tucker (KKT) conditions.In order to leverage among keypoint removal with minimum distortion, suppression of NKG, and complexity, we further present a hybrid scheme of integrating Lagrange multiplier and KKT conditions.On the other hand, KISA is designed via an efficient coarse-to-fine descriptor matching strategy to yield fake feature points so as to create false positives.Experiments, conducted on keypoint removal rate evaluation and an image copy detection method operating on a web-scale image database as a case study, demonstrate the feasibility of our method.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {629–638},
numpages = {10},
keywords = {security, feature extraction, optimization, scale-space, attack, sift},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/3246409,
author = {Wen, Zhen},
title = {Session Details: Full Paper Session 16: Social Media},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3246409},
doi = {10.1145/3246409},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2393436,
author = {Wei, Xiao-Yong and Yang, Zhen-Qun},
title = {Mining In-Class Social Networks for Large-Scale Pedagogical Analysis},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2393436},
doi = {10.1145/2393347.2393436},
abstract = {Modeling the in-class student social networks is a highly desired goal in educational literature. However, due to the difficulty to collect social data, most of the conventional studies can only be conducted in a qualitative way on a small-scale of dataset obtained through questionnaires or interviews. We propose to solve the problems of data collection, social network construction and analysis with multimedia technology, in the way that we can automatically recognize the positions and identities of the students in classroom and construct the in-class social networks accordingly. With the social networks and the statistics on a large-scale dataset, we have demonstrated that the pedagogical analysis for investigating the co-learning patterns among the students can be conducted in a quantitative way, which provides the statistical clues about why prior studies reach conflicting conclusions on the relation between the students' positions in social networks and their academic performances. The experimental results have validated the effectiveness of the proposed approaches in both technical and pedagogical senses.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {639–648},
numpages = {10},
keywords = {pedagogical analysis, in-class social networks},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2393437,
author = {Roy, Suman Deb and Mei, Tao and Zeng, Wenjun and Li, Shipeng},
title = {SocialTransfer: Cross-Domain Transfer Learning from Social Streams for Media Applications},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2393437},
doi = {10.1145/2393347.2393437},
abstract = {The usage and applications of social media have become pervasive. This has enabled an innovative paradigm to solve multimedia problems (e.g., recommendation and popularity prediction), which are otherwise hard to address purely by traditional approaches. In this paper, we investigate how to build a mutual connection among the disparate social media on the Internet, using which cross-domain media recommendation can be realized. We accomplish this goal through SocialTransfer---a novel cross-domain real-time transfer learning framework. While existing transfer learning methods do not address how to utilize the real time social streams, our proposed SocialTransfer is able to effectively learn from social streams to help multimedia applications, assuming an intermediate topic space can be built across domains. It is characterized by two key components: 1) a topic space learned in real time from social streams via Online Streaming Latent Dirichlet Allocation (OSLDA), and 2) a real-time cross-domain graph spectra analysis based transfer learning method that seamlessly incorporates learned topic models from social streams into the transfer learning framework. We present as use cases of emph{SocialTransfer} two video recommendation applications that otherwise can hardly be achieved by conventional media analysis techniques: 1) socialized query suggestion for video search, and 2) socialized video recommendation that features socially trending topical videos. We conduct experiments on a real-world large-scale dataset, including 10.2 million tweets and 5.7 million YouTube videos and show that emph{SocialTransfer} outperforms traditional learners significantly, and plays a natural and interoperable connection across video and social domains, leading to a wide variety of cross-domain applications.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {649–658},
numpages = {10},
keywords = {cross-domain media retrieval, recommendation, transfer learning, social media},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2393438,
author = {Liu, Dong and Ye, Guangnan and Chen, Ching-Ting and Yan, Shuicheng and Chang, Shih-Fu},
title = {Hybrid Social Media Network},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2393438},
doi = {10.1145/2393347.2393438},
abstract = {Analysis and recommendation of multimedia information can be greatly improved if we know the interactions between the content, user, and concept, which can be easily observed from the social media networks. However, there are many heterogeneous entities and relations in such networks, making it difficult to fully represent and exploit the diverse array of information. In this paper, we develop a hybrid social media network, through which the heterogeneous entities and relations are seamlessly integrated and a joint inference procedure across the heterogeneous entities and relations can be developed. The network can be used to generate personalized information recommendation in response to specific targets of interests, e.g., personalized multimedia albums, target advertisement and friend/topic recommendation. In the proposed network, each node denotes an entity and the multiple edges between nodes characterize the diverse relations between the entities (e.g., friends, similar contents, related concepts, favorites, tags, etc). Given a query from a user indicating his/her information needs, a propagation over the hybrid social media network is employed to infer the utility scores of all the entities in the network while learning the edge selection function to activate only a sparse subset of relevant edges, such that the query information can be best propagated along the activated paths. Driven by the intuition that much redundancy exists among the diverse relations, we have developed a robust optimization framework based on several sparsity principles. We show significant performance gains of the proposed method over the state of the art in multimedia retrieval and recommendation using data crawled from social media sites. To the best of our knowledge, this is the first model supporting not only aggregation but also judicious selection of heterogeneous relations in the social media networks.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {659–668},
numpages = {10},
keywords = {edge selective information propagation, hybrid social media network, multimedia retrieval and recommendation},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2393439,
author = {Chen, Yan-Ying and Hsu, Winston H. and Liao, Hong-Yuan Mark},
title = {Discovering Informative Social Subgraphs and Predicting Pairwise Relationships from Group Photos},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2393439},
doi = {10.1145/2393347.2393439},
abstract = {An increasing number of users are contributing the sheer amount of group photos (e.g., for family, classmates, colleagues, etc.) on social media for the purpose of photo sharing and social communication. There arise strong needs for automatically understanding the group types (e.g., family vs. classmates) for recommendation services (e.g., recommending a family-friendly restaurant) and even predicting the pairwise relationships (e.g., mother-child) between the people in the photo for mining implicit social connections. Interestingly, we observe that the group photos are composed of atomic subgroups corresponding to certain social relationships. For this work, we propose a novel framework to (1) connect faces of different attributes and positions as a face graph and (2) discover informative subgraphs to represent social subgroups in group photos. A group photo can be further represented by a bag-of-face-subgraphs (BoFG) -- the occurring frequency of social subgroups, which is informative to categorize specific group types or events. We demonstrate the effectiveness of BoFG in recognizing family photos and achieve 30.5% relative improvement over the state-of-the-art low-level features. Moreover, we propose to predict the pairwise relationships (e.g., husband-wife) in a face graph by the co-occurrence information (e.g., co-occurring with a child) in the mined subgraphs. The experiments demonstrate that the informative social subgroups significantly outperform prior work (36% relatively) which considers merely facial attributes for determining pairwise relationships.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {669–678},
numpages = {10},
keywords = {social relationship, face subgraph mining, face graph},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/3246410,
author = {Del Bimbo, Alberto},
title = {Session Details: Poster Session I},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3246410},
doi = {10.1145/3246410},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396290,
author = {Fang, Yuming and Lin, Weisi and Chen, Zhenzhong and Tsai, Chia-Ming and Lin, Chia-Wen},
title = {Video Saliency Detection in the Compressed Domain},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396290},
doi = {10.1145/2393347.2396290},
abstract = {Saliency detection is widely used to extract the regions of interest in images. Many saliency detection models have been proposed for videos in the uncompressed domain. However, videos are always stored in the compressed domain such as MPEG2, H.264, MPEG4 Visual, etc. In this study, we propose a video saliency detection model based on feature contrast in the compressed domain. Four features of luminance, color, texture and motion are extracted from DCT coefficients and motion vectors in the video bitstream. The static saliency map of video frames is calculated based on the luminance, color and texture features, while the motion saliency map for video frames is computed by motion feature. The final saliency map for video frames is obtained through combining the static saliency map and motion saliency map. Experimental results show good performance of the proposed video saliency detection model in the compressed domain.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {697–700},
numpages = {4},
keywords = {video saliency detection, compressed domain},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396291,
author = {Zhang, Chi and Wang, Weiqiang},
title = {A Robust and Efficient Shot Boundary Detection Approach Based on Fisher Criterion},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396291},
doi = {10.1145/2393347.2396291},
abstract = {In this paper, we present a robust and efficient approach which is capable to simultaneously detect various shot boundaries in a unified way. The proposed approach first detects general shot boundaries based on the idea of Fisher criterion, and then classifies them into two categories, cut and gradual transition (GT), by an SVM classifier. Further computation is performed for the GT shot boundaries to expand rough boundary locations between two frames into the transition interval consisting of all the transitional frames. Finally, a postprocessing module is employed to merge overlapped transitions. The evaluation experiments show that the proposed approach has the impressive performance in both efficiency and accuracy, and outperforms the best results of all the participants of TRECVID 2006.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {701–704},
numpages = {4},
keywords = {shot boundary detection, fisher criterion},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396292,
author = {Wenger, Stephan and Magnor, Marcus},
title = {A Genetic Algorithm for Audio Retargeting},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396292},
doi = {10.1145/2393347.2396292},
abstract = {We present an audio retargeting technique to create custom soundtracks for movies and games from existing audio material (typically music) by automatically rearranging audio segments. Constraints can be specified to make the length of the audio fit the length of a movie scene, or to align parts of a piece of music with particular events. Existing approaches typically create soundtracks with many unnecessary and often disruptive transitions. We extend a recent analysis and resynthesis method with a novel genetic algorithm for finding an optimal succession of audio segments that minimizes the number of audible transitions and repetitions as well as the deviation from user-specified constraints. Compared to prior work, our experiments with audio examples from different musical genres show a significant improvement with respect to the optimization criteria, and the resulting soundtracks contain few, if any, noticeable transitions.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {705–708},
numpages = {4},
keywords = {audio retargeting, audio synthesis, example-based synthesis, sound textures},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396293,
author = {Noh, Hyeonwoo and Han, Bohyung},
title = {Seam Carving with Forward Gradient Difference Maps},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396293},
doi = {10.1145/2393347.2396293},
abstract = {We propose a new energy function for seam carving based on forward gradient differences to preserve regular structures in images. The energy function measures the curvature inconsistency between the pixels that become adjacent after seam removal, and involves the difference of gradient orientation and magnitude of the pixels. Our objective is to minimize the differences induced by the removed seam, and the optimization is performed by dynamic programming based on multiple cumulative energy maps, each of which corresponds to the seam pattern associated with a pixel. The proposed technique preserves straight lines and regular shapes better than the original and improved seam carving, and can be easily combined with other types of energy functions within the seam carving framework. We evaluated the performance of our algorithm by comparing with the original and improved seam carving algorithms using public data.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {709–712},
numpages = {4},
keywords = {seam carving, forward gradient difference maps, image retargetting},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396294,
author = {Chen, Chongyu and Cai, Jianfei and Lin, Weisi and Shi, Guangming},
title = {Surveillance Video Coding via Low-Rank and Sparse Decomposition},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396294},
doi = {10.1145/2393347.2396294},
abstract = {Surveillance videos are usually with a static or gradually changed background. The state-of-the-art block-based codec, H.264/AVC, is not sufficiently efficient for encoding surveillance videos since it cannot exploit the strong background temporal redundancy in a global manner. In this paper, motivated by the recent advance on low-rank and sparse decomposition (LRSD), we propose to apply it for the compression of surveillance videos. In particular, the LRSD is employed to decompose a surveillance video into the low-rank component, representing the background, and the sparse component, representing the moving objects. Then, we design different coding methods for the two different components. We represent the frames of the background by very few independent frames based on their linear dependency, which dramatically removes the temporal redundancy. Experimental results show that, for the compression of surveillance videos, the proposed scheme can significantly outperform H.264/AVC, up to 3 dB PSNR gain, especially at relatively low bit rates.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {713–716},
numpages = {4},
keywords = {low-rank and sparse decomposition, cur decomposition, surveillance video compression},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396295,
author = {Yen, Jui-Yu and Chen, Bo-Hao and Huang, Shih-Chia},
title = {Enhanced Extraction of Moving Objects in Variable Bit-Rate Video Streams},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396295},
doi = {10.1145/2393347.2396295},
abstract = {Motion detection plays an important role in the video surveillance system. Video communications over wireless networks can easily suffer from network congestion or unstable bandwidth, especially for embedded application. A rate control scheme produces various bit-rate video streams to match the available network bandwidth. However, effective detection of moving objects in various bit-rate video streams is a very difficult problem. This paper proposes an advanced approach based on the counter-propagation network through artificial neural networks to achieve effective moving object detection in various bit-rate video streams. We compare our method with other state-of-the-art methods. To demonstrate the performance of our proposed method in regard to object extraction, we analyze qualitative and quantitative comparisons in real-world limited bandwidth networks over a wide range of natural video sequences. The overall results show that our proposed method substantially outperforms other state-of-the-art methods by Similarity and F1 accuracy rates of 73.84% and 84.94%, respectively.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {717–720},
numpages = {4},
keywords = {video surveillance, variable bit-rate, neural network, motion detection},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396296,
author = {Li, Bing and Xiong, Weihua and Hu, Weiming and Ding, Xinmiao},
title = {Context-Aware Affective Images Classification Based on Bilayer Sparse Representation},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396296},
doi = {10.1145/2393347.2396296},
abstract = {In image understanding, the automatic recognition of emotion in an image is becoming important from an applicative viewpoint. Considering the fact that the emotion evoked by an image is not only from its global appearance but also interplays among local regions, we propose a novel context-aware classification model based on bilayer sparse representation (BSR) that simultaneously takes the local context and global-local context into account. The BSR model contains two layers: global sparse representation (GSR) and local sparse representation (LSR). The GSR is to define global similarities between a test image and all training images; while the LSR is to define similarities of local regions' appearances and their co-occurrence between a test image and all training images. The experiments on two data sets demonstrate that our method is effective on affective images classification.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {721–724},
numpages = {4},
keywords = {context-aware, affective image classification, bilayer sparse representation},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396297,
author = {Zhou, Xiuzhuang and Lu, Jiwen and Hu, Junlin and Shang, Yuanyuan},
title = {Gabor-Based Gradient Orientation Pyramid for Kinship Verification under Uncontrolled Environments},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396297},
doi = {10.1145/2393347.2396297},
abstract = {This paper presents a Gabor-based Gradient Orientation Pyramid (GGOP) feature representation method for kinship verification from facial images. First, we perform Gabor wavelet on each face image to obtain a set of Gabor magnitude (GM) feature images from different scales and orientations. Then, we extract the Gradient Orientation Pyramid (GOP) feature of each GM feature image and perform multiple feature fusion for kinship verification. When combined with the discriminative support vector machine (SVM) classifier, GGOP demonstrates the best performance in our experiments, in comparison with several state-of-the-art face feature descriptors. Experimental results are presented to show the efficacy of our proposed approach. Moreover, the performance of our proposed method is also comparable to that of human observers.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {725–728},
numpages = {4},
keywords = {ace analysis, kinship verification, image descriptors},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396298,
author = {Lin, Tao and Lin, Liang and Wang, Qing},
title = {Robust Stroke-Based Video Animation via Layered Motion and Correspondence},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396298},
doi = {10.1145/2393347.2396298},
abstract = {This paper investigates a novel approach to reduce artifacts and visual flickering in generating painterly animations from real video clips. In the traditional painterly animation methods, the brush strokes are propagated over video frames by calculating optical flows, and the visual impression of animations are severely affected by incorrect correspondences. In our method, we combine motion segmentation and occlusion handing to establish accurate dense feature correspondences, which is shown to robust propagate brush strokes against complex motions and occlusions. Moreover, a beforehand rendering strategy is presented to alleviate stroke flickering. In the experiments, we generate a number of animations in cartoon and oil painting style. The quantitative evaluations of brush stabilization is presented as well.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {729–732},
numpages = {4},
keywords = {layered motion, video stylization, non-photorealistic rendering},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396299,
author = {Sun, Wenxiu and Au, Oscar C. and Xu, Lingfeng and Li, Yujun and Hu, Wei and Yu, Zhiding},
title = {Texture Optimization for Seamless View Synthesis through Energy Minimization},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396299},
doi = {10.1145/2393347.2396299},
abstract = {In this paper, we present a view synthesis method named Visto which aims to generate seamless novel views from a monocular view input. We formulate the problem as joint optimization of inter-view texture similarity and geometry preservation, which significantly differs from traditional view synthesis framework. In this way, the image characteristics of virtual view are inherently inherited from the reference view without introducing any image prior or texture modeling technique. The energy function is minimized using Gauss-Seidel-like approach, and the quality of the virtual view is refined iteratively. The proposed approach also tolerates small depth map errors. Further more, the algorithm is parallel friendly. The simulation results outperform several existing state-of-the-art monocular view synthesis systems.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {733–736},
numpages = {4},
keywords = {gauss-seidel-like, inter-view similarity, geometry preservation, texture optimization, seamless view synthesis},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396300,
author = {Xu, Xin-Shun and Jiang, Yuan and Xue, Xiangyang and Zhou, Zhi-Hua},
title = {Semi-Supervised Multi-Instance Multi-Label Learning for Video Annotation Task},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396300},
doi = {10.1145/2393347.2396300},
abstract = {Traditional approaches for automatic video annotation usually represent one video clip with a flat feature vector, neglecting the fact that video data contain natural structures. It is also noteworthy that a video clip is often relevant to multiple concepts. Indeed, the video annotation task is inherently a Multi-Instance Multi-Label learning (MIML) problem. Considering that manually annotating videos is labor-intensive and time-consuming, this paper proposes a semi-supervised MIML approach, SSMIML, which is able to exploit abundant unannotated videos to help improve the annotation performance. This approach takes label correlations into account, and enforces similar instances to share similar multi-labels. Evaluation on TREVID 2005 show that the proposed approach outperforms several state-of-the-art methods.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {737–740},
numpages = {4},
keywords = {semi-supervised learning, multi-instance multi-label learning, video annotation},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396301,
author = {Zhou, Youjie and Luo, Jiebo},
title = {Geo-Location Inference on News Articles via Multimodal PLSA},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396301},
doi = {10.1145/2393347.2396301},
abstract = {The fast evolution and adoption of social media creates an increasingly need for location based services. Location inference on news or events becomes an essential problem. This paper addresses the problem by extracting location involved topics (geo-topic) using both text content and visual content. This paper proposes a geo-topic extraction framework for geo-location inference, including location name entity recognition, location related image association and a multimodal location dependent pLSA geo-topic model. Experiments have shown that our fused model improves the f-score in geo-location inference by 10% compared with single modality based models.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {741–744},
numpages = {4},
keywords = {feature fusion, geo-location inference, multimodal pLSA},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396302,
author = {Mao, Zhendong and Zhang, Yongdong and Gao, Ke and Zhang, DongMing},
title = {A Method for Detecting Salient Regions Using Integrated Features},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396302},
doi = {10.1145/2393347.2396302},
abstract = {We develop a novel algorithm for detecting salient regions. By analyzing the advantages and disadvantages of the existing methods, five principles for designing salient region detection algorithms are summarized. Based on these principles, we propose a novel method that generates saliency map with highlighted salient regions by utilizing two different features, namely visual saliency value and spatial weight. The visual saliency value is determined based on local contrast differences and low-level feature frequencies. The spatial weight is computed by analyzing the size and location of salient regions. Experimental results show that the proposed algorithm outperforms 7 state-of-the-art methods on the public image set.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {745–748},
numpages = {4},
keywords = {integrated features, salient region detection},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396303,
author = {Cai, Xinyuan and Wang, Chunheng and Xiao, Baihua and Chen, Xue and Zhou, Ji},
title = {Deep Nonlinear Metric Learning with Independent Subspace Analysis for Face Verification},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396303},
doi = {10.1145/2393347.2396303},
abstract = {Face verification is the task of determining by analyzing face images, whether a person is who he/she claims to be. It is a very challenge problem, due to large variations in lighting, background, expression, hairstyle and occlusion. The crucial problem is to compute the similarity of two face vectors. Metric learning has provides a viable solution to this problem. Until now, many metric learning algorithms have been proposed, but they are usually limited to learning a linear transformation (i.e. finding a global Mahalanobis metric). In this brief, we propose a nonlinear metric learning method, which learns an explicit mapping from the original space to an optimal subspace, using deep Independent Subspace Analysis network. Compared to kernel methods, which can also learn nonlinear transformations, our method is a deep and local learning architecture, and therefore exhibits more powerful ability to learn the nature of highly variable dataset. We evaluate our method on the LFW benchmark, and results show very comparable performance to the state-of-art methods (achieving 92.28% accuracy), while maintaining simplicity and good generalization ability.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {749–752},
numpages = {4},
keywords = {deep learning architecture, face verification, independent subspace analysis},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396304,
author = {Su, Zhuo and Deng, Daiguo and Yang, Xue and Luo, Xiaonan},
title = {Color Transfer Based on Multiscale Gradient-Aware Decomposition and Color Distribution Mapping},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396304},
doi = {10.1145/2393347.2396304},
abstract = {Automatic global color transfer is a challenging problem in image editing. In this paper, we propose a novel color transfer method, which is based on the gradient-aware decomposition and the color distribution mapping. Firstly, a gradient-aware decomposition model is established to separate the target image into the base and detail layers. Then, the colors of each separated base layer are enforced to match those of a given reference image by Pitie's multi-dimensional probability density function transfer method. After that, all mapped base layers are combined with corresponding boosted detail layers to produce the final output. The experiments demonstrate that our method can achieve a visual satisfied result without post-processing gradient correction.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {753–756},
numpages = {4},
keywords = {gradient-aware decompostion, color transfer, distribution},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396305,
author = {Yang, Yi-Hsuan},
title = {On Sparse and Low-Rank Matrix Decomposition for Singing Voice Separation},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396305},
doi = {10.1145/2393347.2396305},
abstract = {Over recent years there has been a growing interest in finding ways to transform signals/matrices into sparse or low-rank representations, i.e., representations which are sparse in support or of low redundancy. Such decompositions are proving to be particularly powerful for a variety of signal processing and compression problems. In this paper, we investigate the application of this technique to the challenging task of singing voice/accompaniment separation for popular music. The vocal part is modeled as a sparse signal, whereas the instrumental part is considered to be low-rank. In addition, to better account for the particular properties of music, two new algorithms are proposed to improve the decomposition, including the incorporation of harmonicity priors and a back-end drum removal procedure. Evaluations on the MIR-1K benchmark dataset show that the proposed algorithms outperform the state-of-the-art by 0.01-2.41 db.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {757–760},
numpages = {4},
keywords = {low-rank and sparse decomposition, singing voice separation},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396306,
author = {Sun, Xiaoshuai and Yao, Hongxun},
title = {Memorable Basis: Towards Human-Centralized Sparse Representation},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396306},
doi = {10.1145/2393347.2396306},
abstract = {Previous studies of sparse representation in multimedia research focus on developing reliable and efficient dictionary learning algorithms. Despite the sparse prior, how to integrate other related perceptual factors of human being into dictionary learning process was seldom studied. In this paper, we investigate the influence of image memorability for human-centralized sparse representation. Based on the results of a photo memory game, we are able to quantitatively characterize an image's memorability which allows us to train sparse bases from the most memorable images instead of randomly selected natural images. We believed that such kind of basis is more consistent with neural networks in human brain and hence can better predict where human looks. To test our hypothesis, we choose human eye-fixation prediction problem for quantitative evaluation. The experimental results demonstrate the superior performance of our Memorable Basis compared to traditional sparse basis trained from unselected images.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {761–764},
numpages = {4},
keywords = {visual attention, image memorability, eye fixation prediction, human centralized sparse representation},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396307,
author = {Phan, Trung Quy and Shivakumara, Palaiahnakote and Tan, Chew Lim},
title = {Detecting Text in the Real World},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396307},
doi = {10.1145/2393347.2396307},
abstract = {The problem of text detection in natural scene images is challenging because of the unconstrained sizes, colors, backgrounds and alignments of the characters. This paper proposes novel symmetry features for this task. Within a text line, the intra-character symmetry captures the correspondence between the inner contour and the outer contour of a character while the inter-character symmetry helps to extract information from the gap region between two consecutive characters. A formulation based on Gradient Vector Flow is used to detect both types of symmetry points. These points are then grouped into text lines using the consistency in sizes, colors, and stroke and gap thickness. Therefore, unlike most existing methods which use only character features, our method exploits both the text features and the gap features to improve the detection result. Experimentally, our method compares well to the state-of-the-art on public datasets for natural scenes and street-level images, an emerging category of image data. The proposed technique can be used in a wide range of multimedia applications such as content-based image/video retrieval, mobile visual search and sign translation.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {765–768},
numpages = {4},
keywords = {scene text detection, natural scene text, texture analysis, gradient vector flow, symmetry detection, street view images},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396308,
author = {Han, Zhen and Jiang, Junjun and Hu, Ruimin and Lu, Tao and Huang, Kebin},
title = {Face Image Super-Resolution via Nearest Feature Line},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396308},
doi = {10.1145/2393347.2396308},
abstract = {In this paper, we propose a manifold learning based algorithm using 'Nearest Feature Line - NFL' to hallucinate high-resolution face image. According to the fact that existing NFL can effectively characterize the geometrical proportions to the face samples, we propose using NFL metric to define the neighborhood relations between face samples. Our algorithm can solve the problem that traditional method cannot effectively reveal the similar local geometry between high-resolution and low-resolution face manifolds under the condition that the training sample size is small. Moreover, in order to enhance the representation capacity of available face samples and reduce the computational complexity, we select neighborhood samples for each input LR image. Experimental results demonstrate that our algorithm can generates clearer local feature details, and the PSNR is 1.4 dB higher than that of the best manifold learning based method reported so far.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {769–772},
numpages = {4},
keywords = {manifold learning, nearest feature line, super-resolution},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396309,
author = {Fu, Zhenyong and Lu, Hongtao and Ip, Horace H.S. and Lu, Zhiwu},
title = {Modalities Consensus for Multi-Modal Constraint Propagation},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396309},
doi = {10.1145/2393347.2396309},
abstract = {This paper presents a novel modalities consensus framework for multi-modal pairwise constraint propagation (MCP). We first combine multiple single-modal constraint propagation (SCP) problems together, and then explicitly introduce a new modalities consensus regularizer to force the propagation results on different modalities to be consistent with each other. With a separable consensus regularizer, the proposed approach can be effectively solved using an alternating optimization way. More importantly, based on our modalities consensus framework, two single-modal constraint propagation algorithms can be directly reformulated as two well-defined multi-modal solutions. Experimental results on constrained clustering tasks have shown that the proposed framework can achieve significant improvements with respect to the state of the arts.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {773–776},
numpages = {4},
keywords = {pairwise constraint propagation, multi-modal analysis},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396310,
author = {Luo, Ping and Wang, Xiaogang and Lin, Liang and Tang, Xiaoou},
title = {Joint Semantic Segmentation by Searching for Compatible-Competitive References},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396310},
doi = {10.1145/2393347.2396310},
abstract = {This paper presents a framework for semantically segmenting a target image without tags by searching for references in an image database, where all the images are unsegmented but annotated with tags. We jointly segment the target image and its references by optimizing both semantic consistencies within individual images and correspondences between the target image and each of its references. In our framework, we first retrieve two types of references with a semantic-driven scheme: i) the compatible references which share similar global appearance with the target image; and ii) the competitive references which have distinct appearance to the target image but similar tags with one of the compatible references. The two types of references have complementary information for assisting the segmentation of the target image. Then we construct a novel graphical representation, in which the vertices are superpixels extracted from the target image and its references. The segmentation problem is posed as labeling all the vertices with the semantic tags obtained from the references. The method is able to label images without the pixel-level annotation and classifier training, and it outperforms the state-of-the-arts approaches on the MSRC-21 database.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {777–780},
numpages = {4},
keywords = {label propagation, image search, semantic segmentation, scene understanding},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396311,
author = {Chen, Tianlong and Liu, Chunxi and Huang, Qingming},
title = {An Effective Multi-Clue Fusion Approach for Web Video Topic Detection},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396311},
doi = {10.1145/2393347.2396311},
abstract = {The efficient organization and navigation of web videos in the topic level could enhance the user experience and boost the user's understanding about the happened events. Due to the potential application prospects, topic detection attracts increasing research interests in the last decade. On one hand, the user concerned real world hot topic always leads to a massive discussion in the video sharing sites, such as YouTube, Youku, etc. On the other hand, the search volume of the topic related keywords are growing explosively in the search engine such as Google, Yahoo, etc. These keywords are the queries formulated by the users to search their concerned topics. They reflect the users' intention and could be used as a clue to find the hot topics. In this paper, different from the traditional topic detection methods, which mainly rely on data clustering, we propose a novel multi-clue fusion approach for web video topic detection. In our approach, firstly by utilizing the video related tag information, a maximum average score and a burstiness degree are proposed to extract the dense-bursty tag groups. Secondly, the near-duplicate keyframes (NDK) are extracted from the videos and fused with the extracted tag groups. After that, the hot search keywords from the search engine are used as guidance for topic detection. Finally, these clues are combined together to detect the topics hidden in the web video data. Experiment is conducted on the YouTube video data and the results demonstrate that the proposed method is effective.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {781–784},
numpages = {4},
keywords = {topic detection, multi-clues fusion, tag group},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396312,
author = {Liang, Hui and Yuan, Junsong and Thalmann, Daniel},
title = {3D Fingertip and Palm Tracking in Depth Image Sequences},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396312},
doi = {10.1145/2393347.2396312},
abstract = {We present a vision-based approach for robust 3D fingertip and palm tracking on depth images using a single Kinect sensor. First the hand is segmented in the depth images by applying depth and morphological constraints. The palm is located by performing distance transform to the hand contour and tracked with a Kalman filter. The fingertips are detected by combining three depth-based features and tracked with a particle filter over successive frames. Quantitative results on synthetic depth sequences show the proposed scheme can track the fingertips quite accurately. Besides, its capabilities are further demonstrated through a real-life human-computer interaction application.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {785–788},
numpages = {4},
keywords = {geodesic distance, human-computer interaction, kinect sensor, fingertip tracking},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396313,
author = {Mohammadi, Gelareh and Origlia, Antonio and Filippone, Maurizio and Vinciarelli, Alessandro},
title = {From Speech to Personality: Mapping Voice Quality and Intonation into Personality Differences},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396313},
doi = {10.1145/2393347.2396313},
abstract = {From a cognitive point of view, personality perception corresponds to capturing individual differences and can be thought of as positioning the people around us in an ideal personality space. The more similar the personality of two individuals, the closer their position in the space. This work shows that the mutual position of two individuals in the personality space can be inferred from prosodic features. The experiments, based on ordinal regression techniques, have been performed over a corpus of 640 speech samples comprising 322 individuals assessed in terms of personality traits by 11 human judges, which is the largest database of this type in the literature. The results show that the mutual position of two individuals can be predicted with up to 80% accuracy.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {789–792},
numpages = {4},
keywords = {big five personality model, nonverbal vocal behavior, social signal processing, personality assessment},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396314,
author = {Kim, Samuel and Filippone, Maurizio and Valente, Fabio and Vinciarelli, Alessandro},
title = {Predicting the Conflict Level in Television Political Debates: An Approach Based on Crowdsourcing, Nonverbal Communication and Gaussian Processes},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396314},
doi = {10.1145/2393347.2396314},
abstract = {One of the most recent trends in multimedia indexing is to represent data in terms of the social and psychological phenomena that users perceive. In such a perspective this article proposes an approach for the automatic detection of conflict level in television political debates. The proposed approach includes the use of crowdsourcing techniques for modeling the perception of data consumers, the extraction of (language independent) nonverbal behavioral cues and the application of regression techniques based on Gaussian Processes. The experiments have been performed over 1430 clips of 30 seconds extracted from 45 political debates (roughly 12 hours of material). The results show that a correlation up to 0.8 can be achieved between the actual and predicted conflict level.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {793–796},
numpages = {4},
keywords = {nonverbal vocal behavior, social signal processing, conflict, multimedia indexing},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396315,
author = {Bu, Pengyang and Wang, Nan and Ai, Haizhou},
title = {Using Structural Patches Tiling to Guide Human Head-Shoulder Segmentation},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396315},
doi = {10.1145/2393347.2396315},
abstract = {In this paper, we propose a novel and effective structural patches tiling procedure which is able to generate high quality probabilistic masks to guide semantic segmentation.In this structural patches tiling procedure, we first apply a local patch structure classifier trained by random forest to the input image in a sliding window manner, and then construct an MRF iteratively optimized to assemble a high quality probabilistic mask from responses collected from the previous stage.Our main contributions are twofold: A patch-based classification procedure which is fast and capable of capturing rich local structures compared with pixel-based ones; a flexible Markovian sliding window merging algorithm which integrates context information into traditional sliding window method. Without loss of generality, we use head-shoulder segmentation to illustrate this procedure's power. Experiments on daily photos and comparisons with previous work demonstrate that we are able to achieve state-of-the-art head-shoulder segmentation results thanks to this structural patches tiling procedure.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {797–800},
numpages = {4},
keywords = {head-shoulder segmentation, markovian sliding window, patch-based classification},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396316,
author = {Zhang, Bao and Zhao, Handong and Cao, Xiaochun},
title = {Video Object Segmentation with Shortest Path},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396316},
doi = {10.1145/2393347.2396316},
abstract = {Unsupervised video object segmentation is to automatically segment the foreground object in the video without any prior knowledge. This paper proposes an object-level method to segment foreground object, while existing methods are normally based on low level information. We firstly find all the object-like regions. Then based on the corresponding map between the successive frames, the video segmentation problem is converted to graph model one. Rather than adopting TRW-S which might result in a local optimal solution, a shortest path algorithm is explored to get a globally optimum solution. Compared with the state-of-the-art object-level method, our method not only guarantees the continuity of segmentation result but also works well even under the big disturbance of fast motion object in the background. The experimental results on two open datasets (SegTrack and Berkeley Motion Segmentation Dataset) and video sequences captured by ourselves demonstrate the effectiveness of our method.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {801–804},
numpages = {4},
keywords = {video object segmentation, shortest path solution},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396317,
author = {Chen, Ding-Jie and Chen, Hwann-Tzong and Chang, Long-Wen},
title = {Video Object Cosegmentation},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396317},
doi = {10.1145/2393347.2396317},
abstract = {We introduce and address the problem of video object cosegmentation, which concerns the task of segmenting the common object in a pair of video sequences. We present a new algorithm that works on super-voxels in videos to solve this task. The algorithm computes i the intra-video relative motion derived from dense optical flow and ii) the inter-video co-features based on Gaussian mixture models. The experimental results show that, by integrating the intra-video and inter-video information, our algorithm is able to obtain better results of segmenting video objects.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {805–808},
numpages = {4},
keywords = {GMM, co-segmentation, video, motion, graph cut},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396318,
author = {Chen, Zhineng and Ngo, Chong-Wah and Cao, Juan and Zhang, Wei},
title = {Community as a Connector: Associating Faces with Celebrity Names in Web Videos},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396318},
doi = {10.1145/2393347.2396318},
abstract = {Associating celebrity faces appearing in videos with their names is of increasingly importance with the popularity of both celebrity videos and related queries. However, the problem is not yet seriously studied in Web video domain. This paper proposes a Community connected Celebrity Name-Face Association approach (C-CNFA), where the community is regarded as an intermediate connector to facilitate the association. Specifically, with the names and faces extracted from Web videos, C-CNFA decomposes the association task into a three-step framework: community discovering, community matching and celebrity face tagging. To achieve the goal of efficient name-face association under this umbrella, algorithms such as the constrained density-based clustering and exemplar based voting are developed by leveraging different pieces of visual and contextual cues. The evaluation on 0.4 million faces and 144 celebrities shows the effectiveness of the proposed C-CNFA approach. Moreover, using the obtained associations, encouraging results are reported in celebrity video ranking.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {809–812},
numpages = {4},
keywords = {community analysis, name-face association, celebrity videos},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396319,
author = {Petridis, Stavros and Bilakhia, Sanjay and Pantic, Maja},
title = {Comparison of Prediction-Based Fusion and Feature-Level Fusion across Different Learning Models},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396319},
doi = {10.1145/2393347.2396319},
abstract = {There is evidence in neuroscience indicating that prediction of spatial and temporal patterns in the brain plays a key role in perception. This has given rise to prediction-based fusion as a method of combining information from audio and visual modalities. Models are trained on a per-class basis, to learn the mapping from one feature-space to another. When presented with unseen data, each model predicts the respective feature-sets using its learnt mapping, and the prediction error is combined within each class. The model which best describes the audiovisual relationship (by having the lowest combined prediction error) provides its label to the input data. Previous studies have only used neural networks to evaluate this method of combining modalities - this paper extends this to other learning methods, including Long Short-Term Memory recurrent neural networks (LSTMs), Support Vector Machines (SVMs), Relevance Vector Machines (RVMs), and Gaussian Processes (GPs). Our results on cross-database experiments on nonlinguistic vocalisation recognition show that feature-prediction significantly outperforms feature-fusion for neural networks, LSTMs, and GPs, while performance on SVMs and RVMs is more ambiguous and neither model gains an absolute advantage over the other.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {813–816},
numpages = {4},
keywords = {nonlinguistic information processing, prediction-based classification/fusion, audiovisual fusion},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396320,
author = {Rzeszutek, Richard and Phan, Raymond and Androutsos, Dimitrios},
title = {Depth Estimation for Semi-Automatic 2D to 3D Conversion},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396320},
doi = {10.1145/2393347.2396320},
abstract = {The conversion of monoscopic footage into stereoscopic or multiview content is a difficult and time consuming task. A number of semi-automatic methods have been developed to speed up the process and provide some control to the user. However these methods require that the user provide detailed labels indicating the relative depth of objects in the scene. In this paper we present a method to automatically estimate depth in such a way that it is amenable to semi-automatic conversion. The method is designed to simplify the depth labelling task so that the user does not have to provide as many depth labels.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {817–820},
numpages = {4},
keywords = {computer vision, motion estimation, 2D-to-3D conversion, depth estimation},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396321,
author = {Yao, Ting and Ngo, Chong-Wah and Zhu, Shiai},
title = {Predicting Domain Adaptivity: Redo or Recycle?},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396321},
doi = {10.1145/2393347.2396321},
abstract = {Over the years, the academic researchers have contributed various visual concept classifiers. Nevertheless, given a new dataset, most researchers still prefer to develop large number of classifiers from scratch despite expensive labeling efforts and limited computing resources. A valid question is why not multimedia community ``embrace the green" and recycle off-the-shelf classifiers for new dataset. The difficulty originates from the domain gap that there are many different factors that govern the development of a classifier and eventually drive its performance to emphasize certain aspects of dataset. Reapplying a classifier to an unseen dataset may end up GIGO (garbage in, garbage out) and the performance could be much worse than re-developing a new classifier with very few training examples. In this paper, we explore different parameters, including shift of data distribution, visual and context diversities, that may hinder or otherwise encourage the recycling of old classifiers for new dataset. Particularly, we give empirical insights of when to recycle available resources, and when to redo from scratch by completely forgetting the past and train a brand new classifier. Based on these analysis, we further propose an approach for predicting the negative transfer of a concept classifier to a different domain given the observed parameters. Experimental results show that the prediction accuracy of over 75% can be achieved when transferring concept classifiers learnt from LSCOM (news video domain), ImageNet (Web image domain) and Flickr-SF (weakly tagged Web image domain), respectively, to TRECVID 2011 dataset (Web video domain).},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {821–824},
numpages = {4},
keywords = {domain adaptation, cross-domain concept learning},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396322,
author = {Jiang, Xi and Zhang, Tuo and Hu, Xintao and Lu, Lie and Han, Junwei and Guo, Lei and Liu, Tianming},
title = {Music/Speech Classification Using High-Level Features Derived from Fmri Brain Imaging},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396322},
doi = {10.1145/2393347.2396322},
abstract = {With the availability of large amount of audio tracks through a variety of sources and distribution channels, automatic music/speech classification becomes an indispensable tool in social audio websites and online audio communities. However, the accuracy of current acoustic-based low-level feature classification methods is still rather far from satisfaction. The discrepancy between the limited descriptive power of low-level features and the richness of high-level semantics perceived by the human brain has become the 'bottleneck' problem in audio signal analysis. In this paper, functional magnetic resonance imaging (fMRI) which monitors the human brain's response under the natural stimulus of music/speech listening is used as high-level features in the brain imaging space (BIS). We developed a computational framework to model the relationships between BIS features and low-level features in the training dataset with fMRI scans, predict BIS features of testing dataset without fMRI scans, and use the predicted BIS features for music/speech classification in the application stage. Experimental results demonstrated the significantly improved performance of music/speech classification via predicted BIS features than that via the original low-level features.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {825–828},
numpages = {4},
keywords = {music/speech classification, brain imaging space, functional magnetic resonance imaging, semantic gap},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396323,
author = {Liu, Jen-Yu and Yeh, Chin-Chia and Yang, Yi-Hsuan and Teng, Yuan-Ching},
title = {Bilingual Analysis of Song Lyrics and Audio Words},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396323},
doi = {10.1145/2393347.2396323},
abstract = {Thanks to the development of music audio analysis, state-of-the-art techniques can now detect musical attributes such as timbre, rhythm, and pitch with certain level of reliability and effectiveness. An emerging body of research has begun to model the high-level perceptual properties of music listening, including the mood and the preferable listening context of a music piece. Towards this goal, we propose a novel text-like feature representation that encodes the rich and time-varying information of music using a composite of features extracted from the song lyrics and audio signals. In particular, we investigate dictionary learning algorithms to optimize the generation of local feature descriptors and also probabilistic topic models to group semantically relevant text and audio words. This text-like representation leads to significant improvement in automatic mood classification over conventional audio features.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {829–832},
numpages = {4},
keywords = {topic model, MLLDA, LDA, sparse coding, audioword},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396324,
author = {Tang, Ye and Yang, Yu-Bin and Gao, Yang},
title = {Self-Paced Dictionary Learning for Image Classification},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396324},
doi = {10.1145/2393347.2396324},
abstract = {Image classification is an important research task in multimedia content analysis and processing. Learning a compact dictionary easying to derive sparse representation is one of the focused issues in the state-of-the-art image classification framework. Most existing dictionary learning approaches assign equal importance to all training samples, which in fact have different complexity in terms of sparse representation. Meanwhile, the contextual information "hidden" in different samples is ignored as well. In this paper, we propose a self-paced dictionary learning algorithm in order to accommodate the "hidden" information of the samples into the learning procedure, which uses the easy samples to train the dictionary first, and then iteratively introduces more complex samples in the remaining training procedure until the entire training data are all easy samples. The algorithm adaptively chooses the easy samples in each iteration, while the learned dictionary in the previous iteration is in turn used as a basis for the current iteration. This strategy implicitly takes advantage of the contextual relationships among training samples. The number of the chosen samples in each iteration is determined by an adaptive threshold function proposed in this paper. Experimental results on benchmark datasets, including Caltech-101 and 15-Scene, show that our algorithm leads to better dictionary representation and classification performance than the baseline methods.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {833–836},
numpages = {4},
keywords = {self-paced, image classification, dictionary learning},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396325,
author = {Wu, Xixuan and Qiao, Yu and Wang, Xiaogang and Tang, Xiaoou},
title = {Cross Matching of Music and Image},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396325},
doi = {10.1145/2393347.2396325},
abstract = {Human perception of music and image are highly correlated. Both of them can inspire human sensation like emotion and power. This paper investigates how to model the relationship between music and image using 47,888 music-image pairs extracted from music videos. We have two basic observations for this relationship: 1) music space exhibits simpler cluster structure than image space, and 2) the relationship between the two spaces is complex and nonlinear. Based on these observations, we develop Multiple Ranking Canonical Correlation Analysis (MR-CCA) to learn such relationship. MR-CCA clusters the music-image pairs according to their music parts, and then conducts Ranking CCA (R-CCA) for each cluster. Compared with classical CCA, R-CCA takes account of the pairwise ranking information available in our dataset. MR-CCA improves performance and significantly reduce computational cost. Experiment results show that R-CCA outperforms CCA, and MR-CCA has the best performance with a consistency score of 84.52% with human labeling. The proposed method can be generalized to model cross media relationship and has potential applications in video generation, background music recommendation, and joint retrieval of music and image.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {837–840},
numpages = {4},
keywords = {ranking canonical correlation analysis, combination of clusters, music-image similarity},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396326,
author = {Peters, Nils and Lei, Howard and Friedland, Gerald},
title = {Name That Room: Room Identification Using Acoustic Features in a Recording},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396326},
doi = {10.1145/2393347.2396326},
abstract = {This paper presents a system for identifying the room in an audio or video recording through the analysis of acoustical properties. The room identification system was tested using a corpus of 13440 reverberant audio samples. With no common content between the training and testing data, an accuracy of 61% for musical signals and 85% for speech signals was achieved. This approach could be applied in a variety of scenarios where knowledge about the acoustical environment is desired, such as location estimation, music recommendation, or emergency response systems.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {841–844},
numpages = {4},
keywords = {media content analysis and processing},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396327,
author = {Wong, Lai-Kuan and Wong, Kok-Lim},
title = {Enhancing Visual Dominance by Semantics-Preserving Image Recomposition},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396327},
doi = {10.1145/2393347.2396327},
abstract = {We present a semi-automatic photographic recomposition approach that employs a semantics-preserving warp of the input image to enhance the visual dominance of the main subjects. Our method uses the tearable image warping method to shift the subjects against the background (and vice versa), so that their visual dominance is improved, and yet preserve the desired spatial semantics between the subjects and the background. The recomposition is guided by a measure of the resulting visual dominance of the main subjects. Our user experiment shows the effectiveness of the approach.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {845–848},
numpages = {4},
keywords = {visual dominance, tearable image warping, image semantics, computational aesthetics, photographic recomposition},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396328,
author = {Xiao, Jie and Zhou, Wengang and Li, Xia and Wang, Meng and Tian, Qi},
title = {Image Tag Re-Ranking by Coupled Probability Transition},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396328},
doi = {10.1145/2393347.2396328},
abstract = {The large amount of user-tagged images on social networks is helpful to facilitate image management and image search. However, many tags are weakly relevant or irrelevant to the visual content, resulting in unsatisfactory performance in tag related applications. In this paper, we propose a coupled probability transition algorithm to estimate the text-visual group relevance from the observed data and then leverage it to predict tag relevance for a new query image. The visual group for a given tag is a cluster of images that are visually similar and share the same tag. The tag-visual group relevance is uncovered by exploiting the mutual reinforcement in visual space and semantic space alternatively. Experiments on NUS-WIDE dataset show the validity and superiority of the proposed approach over existing methods.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {849–852},
numpages = {4},
keywords = {image tag re-ranking, coupled probability transition},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396329,
author = {Li, Zechao and Liu, Jing and Jiang, Yu and Tang, Jinhui and Lu, Hanqing},
title = {Low Rank Metric Learning for Social Image Retrieval},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396329},
doi = {10.1145/2393347.2396329},
abstract = {With the popularity of social media applications, large amounts of social images associated with rich context are available, which is helpful for many applications. In this paper, we propose a Low Rank distance Metric Learning (LRML) algorithm by discovering knowledge from these rich contextual data, to boost the performance of CBIR. Different from traditional approaches that often use the must-links and cannot-links between images, the proposed method exploits information from the visual and textual domains. We assume that the visual similarity estimated by the learned metric is expected to be consistent with the semantic similarity in the textual domain. Since tags are usually noisy, misspelling or meaningless, we also leverage the preservation of visual structure to prevent overfitting those noisy tags. On the other hand, the metric is straightforward constrained to be low rank. We formulate it as a convex optimization problem with nuclear norm minimization and propose an effective optimization algorithm based on proximal gradient method. With the learned metric for image retrieval, some experimental evaluations on a real-world dataset demonstrate the outperformance of our approach over other related work.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {853–856},
numpages = {4},
keywords = {social images retrieval, metric learning, low rank},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396330,
author = {Jia, Jia and Wu, Sen and Wang, Xiaohui and Hu, Peiyun and Cai, Lianhong and Tang, Jie},
title = {Can We Understand van Gogh's Mood? Learning to Infer Affects from Images in Social Networks},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396330},
doi = {10.1145/2393347.2396330},
abstract = {Can we understand van Gogh's mood from his artworks? For many years, people have tried to capture van Gogh's affects from his artworks so as to understand the essential meaning behind the images and catch on why van Gogh created these works. In this paper, we study the problem of inferring affects from images in social networks. In particular, we aim to answer: What are the fundamental features that reflect the affects of the authors in images? How the social network information can be leveraged to help detect these affects? We propose a semi-supervised framework to formulate the problem into a factor graph model. Experiments on 20,000 random-download Flickr images show that our method can achieve a precision of 49% with a recall of 24% on inferring authors'affects into 16 categories. Finally, we demonstrate the effectiveness of the proposed method on automatically understanding van Gogh's Mood from his artworks, and inferring the trend of public affects around special event.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {857–860},
numpages = {4},
keywords = {emotion, color features, image, affects, factor graph model},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396331,
author = {Liu, Yang and Liu, Jing and Li, Zechao and Niu, Biao and Lu, Hanqing},
title = {Social Tag Alignment with Image Regions by Sparse Reconstructions},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396331},
doi = {10.1145/2393347.2396331},
abstract = {How to align social tags with image regions without additional human intervention is a challenging but a valuable task since it can provide more detailed image semantic information and improve the accuracy of image retrieval. To this end, we propose a novel tag-to-region method with two phases of sparse reconstructions by exploring the large-scale user contributed resources. Given an image with social tags, we first explore the tagging information of large-scale social images to sparsely reconstruct the label vector of the given image, and then use the reconstructing weights as the semantic relevance to the image. With the top $T$ semantically relevant images, we further employ a group sparse coding algorithm to reconstruct each region of the given image, in which the regions from the social images with a common label are deemed as a label group. The group sparsity works on the assumption that one image region corresponds to tags as few as possible. Finally, the region-level tags can be predicted based on the reconstruction error in the corresponding label groups. Extensive experiments on MSRC and SAIAPR TC-12 datasets demonstrate the encouraging performance of our method in comparison with other baselines.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {861–864},
numpages = {4},
keywords = {sparse coding, image region-tag alignment},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396332,
author = {Wang, Yanxiang and Sundaram, Hari and Xie, Lexing},
title = {Social Event Detection with Interaction Graph Modeling},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396332},
doi = {10.1145/2393347.2396332},
abstract = {This paper focuses on detecting social, physical-world events from photos posted on social media sites. The problem is important: cheap media capture devices have significantly increased the number of photos shared on these sites. The main contribution of this paper is to incorporate online social interaction features in the detection of physical events. We believe that online social interaction reflect important signals among the participants on the "social affinity" of two photos, thereby helping event detection. We compute social affinity via a random-walk on a social interaction graph to determine similarity between two photos on the graph. We train a support vector machine classifier to combine the social affinity between photos and photo-centric metadata including time, location, tags and description. Incremental clustering is then used to group photos to event clusters. We have very good results on two large scale real-world datasets: Upcoming and MediaEval. We show an improvement between 0.06-0.10 in F1 on these datasets.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {865–868},
numpages = {4},
keywords = {social media, similarity metric, event detection},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/3246411,
author = {Cao, Liangliang},
title = {Session Details: Poster Session 2},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3246411},
doi = {10.1145/3246411},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396334,
author = {Bian, Jingwen and Zha, Zheng-Jun and Zhang, Hanwang and Tian, Qi and Chua, Tat-Seng},
title = {Visual Query Attributes Suggestion},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396334},
doi = {10.1145/2393347.2396334},
abstract = {Query suggestion is an effective solution to help users deliver their search intent. While many query suggestion approaches have been proposed for test-based image retrieval with query-by-keywords, query suggestion for content-based image retrieval (CBIR) with query-by-example (QBE) has been seldom studied. QBE usually suffers from the "intention gap" problem, especially when the user fails to get an appropriate query image to express his search intention precisely. In this paper, we propose a novel query suggestion scheme named Visual Query Attributes Suggestion (VQAS) for image search with QBE. Given a query image, informative attributes are suggested to the user as complements to the query. These attributes reflect the visual properties and key components of the query. By selecting some suggested attributes, the user can provide more precise search intent which is not captured by the query image. The evaluation results on two real-world image datasets show the effectiveness of VQAS in terms of retrieval performance and the quality of query suggestions.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {869–872},
numpages = {4},
keywords = {query suggestion, image search, attribute},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396335,
author = {Cai, Junjie and Zha, Zheng-Jun and Zhou, Wengang and Tian, Qi},
title = {Attribute-Assisted Reranking for Web Image Retrieval},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396335},
doi = {10.1145/2393347.2396335},
abstract = {Image search reranking is an effective approach to refine the text-based image search result. Most existing reranking approaches are based on low-level visual features. In this paper, we propose to exploit semantic attributes for image search reranking. Based on the classifiers for all the pre-defined attributes, each image is represented by a attribute feature consisting of the responses from these classifiers. A hypergraph is then used to model the relationship between images by integrating low-level visual features and attribute features. Hypergraph ranking is performed to order the images. Its basic principle is that visually similar images should have similar ranking scores. We conduct experiments on 300 queries in MSRA-MM V2.0 dataset. The experimental results demonstrate the effectiveness of our approach.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {873–876},
numpages = {4},
keywords = {image search reranking, attribute},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396336,
author = {Li, Xia and Zhou, Wengang and Tang, Jinhui and Tian, Qi},
title = {Query Expansion Enhancement by Fast Binary Matching},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396336},
doi = {10.1145/2393347.2396336},
abstract = {Query expansion has been successfully employed to improve the performance of image retrieval system. It usually expands the original query based on the information from top ranked images. However, it may fail when some of the top ranked images are false positive or contain noisy features. To minimize the amount of irrelevant local features introduced, we propose to enhance query expansion by fast binary matching. More specifically, the noisy points on a candidate image are filtered out by local verification with their mapped locations on the query image. We further rank the expansion results by three different measurements based on local patch similarity in the image space. Experiments on partial-duplicate Web image search with a database of one million images show that the proposed approach achieves promising improvement in mean Average Precision (mAP) over the state-of-the-art query expansion approaches, and remains efficient in search time.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {877–880},
numpages = {4},
keywords = {query expansion, image retrieval},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396337,
author = {Liu, Xianglong and He, Junfeng and Liu, Di and Lang, Bo},
title = {Compact Kernel Hashing with Multiple Features},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396337},
doi = {10.1145/2393347.2396337},
abstract = {Hashing methods, which generate binary codes to preserve certain similarity, recently have become attractive in many applications like large scale visual search. However, most of state-of-the-art hashing methods only utilize single feature type, while combining multiple features has been proved very helpful in image search. In this paper we propose a novel hashing approach that utilizes the information conveyed by different features. The multiple feature hashing can be formulated as a similarity preserving problem with optimal linearly-combined multiple kernels. Such formulation is not only compatible with general types of data and diverse types of similarities indicated by different visual features, but also helpful to achieve fast training and search. We present an efficient alternating optimization to learn the hashing functions and the optimal kernel combination. Experimental results on two well-known benchmarks CIFAR-10 and NUS-WIDE show that the proposed method can achieve 11% and 34% performance gains over state-of-the-art methods.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {881–884},
numpages = {4},
keywords = {compact hashing, multiple feature, multiple kernel},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396338,
author = {Tu, Weiwen and Pan, Rong and Wang, Jingdong},
title = {Similar Image Search with a Tiny Bag-of-Delegates Representation},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396338},
doi = {10.1145/2393347.2396338},
abstract = {Similar image search over a large image database has been attracting a lot of attention recently. The widely-used solution is to use a set of codes, which we call bag-of-delegates, to represent each image, and to build inverted indices to organize the image database. The search can be conducted through the inverted indices, which is the same to the way of using texts to index images for search and has been shown to be efficient and effective.In this paper, we propose a tiny bag-of-delegates representation that uses a small amount of delegates with a high search performance guaranteed. The main advantage is that less storageis required to save the inverted indices while having a high search accuracy. We propose an adaptive forward selection scheme to sequentially learn more and more inverted indices that are constructed based on subspace partition, e.g. using spatial partition trees. Experimental results demonstrate that our approach can require a smaller number of delegates while achieving the same accuracy and taking similar time.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {885–888},
numpages = {4},
keywords = {adaptive forward selection, spatial partition trees, similar image search, bag-of-delegates},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396339,
author = {Cao, Chen and Chen, Shifeng and Li, Yuhong and Liu, Jianzhuang},
title = {Online Non-Feedback Image Re-Ranking via Dominant Data Selection},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396339},
doi = {10.1145/2393347.2396339},
abstract = {Image re-ranking aims at improving the precision of keyword-based image retrieval, mainly by introducing visual features to re-rank. Many existing approaches require offline training for every keyword, which are unsuitable for online image search. Other real-time approaches demand user interaction, which are inappropriate for large-scale image collection. To improve the accuracy of web image retrieval in a practicable manner, we propose a novel re-ranking algorithm to explore the cluster information of the image set. First, we build spectral graph on images that retrieved bysearch engine, and remove isolated nodes as noisy images. Then, we select positive samples from the most dominant cluster in initial top-ranked images, and the samples are used for semi-supervised learning and ranking. Our algorithm is online and non-feedback. Experiments on two public databases demonstrate that our algorithm outperforms the state-of-the-art approaches.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {889–892},
numpages = {4},
keywords = {spectral ranking, image re-ranking},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396340,
author = {Zhao, Kun and Liu, Wei and Liu, Jianzhuang},
title = {Optimal Semi-Supervised Metric Learning for Image Retrieval},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396340},
doi = {10.1145/2393347.2396340},
abstract = {In a typical content-based image retrieval (CBIR) system, images are represented as vectors and similarities between images are measured by a specified distance metric. However, the traditional Euclidean distance cannot always deliver satisfactory performance, so an effective metric sensible to the input data is desired. Tremendous recent works on metric learning have exhibited promising performance, but most of them suffer from limited label information and expensive training costs. In this paper, we propose two novel metric learning approaches, Optimal Semi-Supervised Metric Learning and its kernelized version. In the proposed approaches, we incorporate information from both labeled and unlabeled data to design a convex and computationally tractable learning framework which results in a globally optimal solution to the target metric of much lower rank than the original data dimension. Experiments on several image benchmarks demonstrate that our approaches lead to consistently better distance metrics than the state-of-the-arts in terms of accuracy for image retrieval.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {893–896},
numpages = {4},
keywords = {semi-supervised learning, metric learning, image retrieval},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396341,
author = {Wen, Yue and Gao, Yue and Hong, Richang and Luan, Huanbo and Liu, Qiong and Shen, Jialie and Ji, Rongrong},
title = {View-Based 3D Object Retrieval by Bipartite Graph Matching},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396341},
doi = {10.1145/2393347.2396341},
abstract = {Bipartite graph matching has been investigated in multiple view matching for 3D object retrieval. However, existing methods employ one-to-one vertex matching scheme while more than two views may share close semantic meanings in practice. In this work, we propose a bipartite graph matching method to measure the distance between two objects based on multiple views. In the proposed method, representative views are first selected by using view clustering for each object, and the corresponding weights are given based on the cluster results. A bipartite graph is constructed by using the two groups of representative views from two compared objects. To calculate the similarity between two objects, the bipartite graph is first partitioned to several subsets, and the views in the same sub-set are with high possibility to be with similar semantic meanings. The distances between two objects within individual subsets are then assembled through the graph to obtain the final similarity. Experimental results and comparison with the state-of-the-art methods demonstrate the effectiveness of the proposed algorithm.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {897–900},
numpages = {4},
keywords = {3D object retrieval, graph matching, bipartite graph},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396342,
author = {Ohbuchi, Ryutarou and Kurita, Yukinori},
title = {Local Geometry Adaptive Manifold Re-Ranking for Shape-Based 3D Object Retrieval},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396342},
doi = {10.1145/2393347.2396342},
abstract = {This paper proposes an improvement to Manifold Ranking algorithm used for search results ranking in the context of shape-based 3D model retrieval. Manifold Ranking algorithm by Zhou et al estimates, given a set of high-dimensional feature vectors, a lower-dimensional manifold on which the features lie. It then computes diffusion-based distances from a feature vector (or feature vectors) to the other feature vectors on the manifold. When applied to content-based retrieval, overall retrieval accuracy is significantly better than a "simple" fixed distance metric. However, in a small neighborhood of query, retrieval ranks obtained by a "simple" distance metric (e.g., L1-norm) performs better than those obtained by Manifold Ranking. Proposed re-ranking algorithm tries to combine ranking results due to both simple distance metric and Manifold Ranking in an automatic query expansion framework for better ranking results. Experimental evaluation has shown that the proposed method is effective in improving retrieval accuracy.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {901–904},
numpages = {4},
keywords = {manifold ranking, distance metric learning, content-based retrieval, automatic query expansion, 3d model retrieval},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396343,
author = {Ye, Junfeng and Chen, Jia and Chen, Zejia and Zhu, Yihe and Bao, Shenghua and Su, Zhong and Yu, Yong},
title = {DLMSearch: Diversified Landmark Search by Photo},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396343},
doi = {10.1145/2393347.2396343},
abstract = {This paper focuses on the problem of searching for diversified landmarks with photos. More particularly, we propose a system called DLMSearch which handles image query, searches for diversified landmarks and provides representative visual summaries. DLMSearch allows a user to upload a query photo and searches for landmarks with high relevance and diversity in real time. Then DLMSearch presents a delicate photo summary for each returned landmark, considering both visual representativeness and diversity. Quantative evaluations on a web-scale landmark photo collection demonstrate the effectiveness of the DLMSearch system. Experimental results verify the merits of the proposed system.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {905–908},
numpages = {4},
keywords = {content-based image retrieval, landmark search, visual summary, diversity},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396344,
author = {Fu, Hao and Qiu, Guoping},
title = {Fast Semantic Image Retrieval Based on Random Forest},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396344},
doi = {10.1145/2393347.2396344},
abstract = {This paper introduces random forest as a computational and data structure paradigm for fusing low-level visual features and high-level semantic concepts for image retrieval. We use visual features to split the tree nodes and use the image labels to supervise the splitting to make images located at the same tree node share similar semantic concepts as well as visual similarities. We exploit such a random forest and define the semantic neighbor set (SNS) of a given image as the union of all images in the leaf nodes that this image falls onto. From SNS we further define the semantic similarity measure (SSM) between two images as the number of trees in which they share the same leaf nodes within a SNS. With SNS and SSM, example-based image retrieval becomes that of first finding the SNS of the querying image and then ranking the images according to the SSMs between the querying image and images in its SNS. We also show that the new technique can be adapted for keyword-based semantic image retrieval. The inherent efficient tree data structure leads to fast solutions. We will present experimental results to show the effectiveness of this new semantic image retrieval technique.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {909–912},
numpages = {4},
keywords = {random forest, semantic image retrieval, semantic nearest neighbor},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396345,
author = {Tseng, Kai-Yu and Lin, Yen-Liang and Chen, Yu-Hsiu and Hsu, Winston H.},
title = {Sketch-Based Image Retrieval on Mobile Devices Using Compact Hash Bits},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396345},
doi = {10.1145/2393347.2396345},
abstract = {The advent of touch panels in mobile devices has provided a good platform for mobile sketch search. However, most of the previous sketch image retrieval systems usually adopt an inverted index structure on large-scale image database, which is formidable to be operated in the limited memory of mobile devices. In this paper, we propose a novel approach to address these challenges. First, we effectively utilize distance transform (DT) features to bridge the gap between query sketches and natural images. Then these high-dimensional DT features are further projected to more compact binary hash bits. The experimental results show that our method achieves very competitive retrieval performance with MindFinder approach [3] but only requires much less memory storage (e.g., our method only requires 3% of total memory storage of MindFinder in 2.1 million images). Due to its low consumption of memory, the whole system can independently operate on the mobile devices.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {913–916},
numpages = {4},
keywords = {sketch, hash},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396346,
author = {Feng, Songhe and Lang, Congyan and Li, Bing},
title = {Towards Relevance and Saliency Ranking of Image Tags},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396346},
doi = {10.1145/2393347.2396346},
abstract = {Social image tag ranking has emerged as an important research topic recently due to its potential application on web image search. This paper presents an adaptive all-season tag ranking algorithm which can handle the images with and without distinct object(s) using different tag ranking strategies. Firstly, based on saliency map derived from the visual attention model, a linear SVM is trained to pre-classify an image as attentive or non-attentive category by using the gray histogram descriptor on the corresponding saliency map. Then, an image with distinct object is processed by an attention-driven tag saliency ranking algorithm emphasizing distinct object. On the other hand, an image without distinct object is processed by the tag relevance ranking algorithm via the sparse representation based neighbor-voting strategy. Such adaptive ranking strategy can be regarded as taking full advantage of existing tag ranking paradigms. Experiments conducted on well-known image data sets demonstrate the effectiveness and efficiency of the proposed framework.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {917–920},
numpages = {4},
keywords = {visual attention model, adaptive tag ranking, multiple-instance learning, sparse representation},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396347,
author = {Pan, Yi-Feng and Sun, Jian and Chen, Siyuan and He, Yuan and Xia, Yingju and Sun, Jun and Naoi, Satoshi},
title = {Mobile-Based Advertisement Information Retrieval from Images and Websites},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396347},
doi = {10.1145/2393347.2396347},
abstract = {In the real world, there are a huge amount of advertisement (ad) boards to make customers have a visual awareness of the products or services easily. However, information appearing in the ad boards is so limited that customers always want to know more ad details in a convenient way. In this paper, we present an mobile-based prototype system to automatically extract web ad information from images and websites. After capturing ad images by smartphones and sending them to a remote server, ad image text is recognized by OCR engine, from where ad phrases and keywords are extracted and combined together as queries. Ad web page candidates are then obtained by specific search engines and clustered to remove noises. OCR results are further used to estimate valid ad topic web pages which are pushed back to end users for searching more detailed ad information. Based on the experiments on a real-world ad image dataset collected by ourselves, true ad topic web pages can be found from top-one and top-ten returned pages in about 51.85% and 83.33% query images respectively, which illustrates the effectiveness of the proposed system.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {921–924},
numpages = {4},
keywords = {advertisement information retrieval, mobile visual search},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396348,
author = {Ahlstr\"{o}m, David and Hudelist, Marco A. and Schoeffmann, Klaus and Schaefer, Gerald},
title = {A User Study on Image Browsing on Touchscreens},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396348},
doi = {10.1145/2393347.2396348},
abstract = {Default image browsing interfaces on touch-based mobile devices provide limited support for image search tasks. To facilitate fast and convenient searches we propose an alternative interface that takes advantage of 3D graphics and arranges images on a rotatable globe according to color similarity. In a user study we compare the new design to the iPad's image browser. Results collected from 24 participants show that for color-sorted image collections the globe can reduce search time by 23% without causing more errors and that it is perceived as being fun to use and preferred over the standard browsing interface by 70% of the participants.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {925–928},
numpages = {4},
keywords = {image browsing and search, mobile devices, small screens},
location = {Nara, Japan},
series = {MM '12}
}

@inproceedings{10.1145/2393347.2396349,
author = {Xiao, Yanhui and Zhu, Zhenfeng and Wei, Shikui and Zhao, Yao},
title = {Discriminative ICA Model with Reconstruction Constraint for Image Classification},
year = {2012},
isbn = {9781450310895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2393347.2396349},
doi = {10.1145/2393347.2396349},
abstract = {Independent Component Analysis (ICA) is an effective unsupervised tool to learn statistically independent representations. However, ICA is not only sensitive to whitening but also difficult to learn an over-complete basis set. Consequently, ICA with soft Reconstruction cost(RICA) was presented to learn sparse representations with over-complete basis even on unwhitened data. Nevertheless, this model may not be an optimal discriminative model for classification tasks, because it failed to consider the association between the training sample and its class. In this paper, we propose a supervised Discriminative ICA model with Reconstruction constraint for image classification, named DRICA. DRICA brings in class information to learn the over-complete basis by incorporating inhomogeneous representation cost constraint into the RICA framework. This constraint leads to partition the set of basis vectors into several subsets corresponding to the sample classes, where each subset could sparsely model data samples from the same class but not others. Therefore, the proposed ICA model can learn an over-complete basis and an optimal multi-class classifier jointly. Some experiments carried out on several standard image databases validate the effectiveness of DRICA for image classification.},
booktitle = {Proceedings of the 20th ACM International Conference on Multimedia},
pages = {929–932},
numpages = {4},

license_type: permissive

extension: bib