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Ming Zhao

Ming Zhao

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    YouTubeEvent: On Large-Scale Video Event Classification
    Bingbing Ni
    The 3rd International Workshop on Video Event Categorization, Tagging and Retrieval for Real-World Applications at IEEE ICCV'2011
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    YouTubeCat: Learning to Categorize Wild Web Videos
    Zheshen Wang
    Baoxin Li
    IEEE Conf on Computer Vision and Pattern Recognition (CVPR) (2010)
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    Taxonomic Classification for Web-based Videos
    Xiaoyun Wu
    IEEE Conf on Computer Vision and Pattern Recognition (CVPR), IEEE (2010)
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    A Large-Scale Taxonomic Classification System for Web-based Videos
    Reto Strobl
    John Zhang
    the 11th European Conference on Computer Vision (ECCV 2010)
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    Tour the World: building a web-scale landmark recognition engine
    Yantao Zheng
    Ulrich Buddemeier
    Fernando Brucher
    Tat-Seng Chua
    International Conference on Computer Vision and Pattern Recognition (CVPR) (2009)
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    Tour the world: a technical demonstration of a web-scale landmark recognition engine
    Yan-Tao Zheng
    Ulrich Buddemeier
    Fernando Brucher
    Tat-Seng Chua
    MM '09: Proceedings of the seventeen ACM international conference on Multimedia, ACM, New York, NY, USA (2009), pp. 961-962
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    Audiovisual Celebrity Recognition in Unconstrained Web Videos
    Pedro Moreno
    Proceedings of the IEEE Conference on Acoustics, Speech, and Signal Processing (ICASSP) (2009)
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    Preview abstract A typical automatic face recognition system is composed of three parts: face detection, face alignment and face recognition. Conventionally, these three parts are processed in a bottom-up manner: face detection is performed first, then the results are passed to face alignment, and finally to face recognition. The bottom-up approach is one extreme of vision approaches. The other extreme approach is top-down. In this paper, we proposed a stochastic mixture approach for combining bottom-up and top-down face recognition: face recognition is performed from the results of face alignment in a bottom-up way, and face alignment is performed based on the results of face recognition in a top-down way. By modeling the mixture face recognition as a stochastic process, the recognized person is decided probabilistically according to the probability distribution coming from the stochastic face recognition, and the recognition problem becomes that “who the most probable person is when the stochastic process of face recognition goes on for a long time or ideally for an infinite duration”. This problem is solved with the theory of Markov chains by modeling the stochastic process of face recognition as a Markov chain. As conventional face alignment is not suitable for this mixture approach, discriminative face alignment is proposed. And we also prove that the stochastic mixture face recognition results only depend on discriminative face alignment, not on conventional face alignment. The effectiveness of our approach is shown by extensive experiments. View details
    Visual Synset: Towards a Higher-level Visual Representation
    Yantao Zheng
    Shi-Yong Neo
    Tat-Seng Chua
    Qi Tian
    CVPR (2008)
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    Exploring Knowledge of Sub-domain in a Multi-resolution Bootstrapping Framework for Concept Detection in News Video
    Gang Wang
    Tat-Seng Chua
    ACM International Conference on Multimedia (MM) (2008)
    Combining Metadata and Context Information in Annotating Personal Media
    Tat-Seng Chua
    Ramesh Jain
    International Workshop on Advanced Image Technology (IWAIT) (2007)
    Face alignment with unified subspace optimization for active statistical models
    Tat-Seng Chua
    IEEE International Conference on Automatic Face and Gesture Recognition (FG) (2006)
    TRECVID 2006 by NUS-I2R
    Tat-Seng Chua
    Shi-Yong Neo
    Yantao Zheng
    Hai-Kiat Goh
    Sheng Tang
    Yang Xiao
    TRECVID (2006)
    Morphable face reconstruction with multiple images
    Tat-Seng Chua
    Terence Sim
    EEE International Conference on Automatic Face and Gesture Recognition (FG) (2006)
    Automatic Person Annotation of Family Photo Album
    Yong Teo
    Siliang Liu
    Tat-Seng Chua
    Ramesh Jain
    International Conference on Image and Video Retrieval (CIVR) (2006), pp. 163-172
    Multi-faceted contextual model for person identification in news video
    Shi-Yong Neo
    Hai-Kiat Goh
    Tat-Seng Chua
    International Multimedia Modelling Conference (MMM) (2006)
    TRECVID 2005 by NUS PRIS
    Tat-Seng Chua
    Shi-Yong Neo
    Hai-Kiat Goh
    Yang Xiao
    Gang Wang
    Sheng Gao
    Kai Chen
    TRECVID (2005)
    Shape evaluation for weighted active shape models
    Stan Z. Li
    Chun Chen
    Jiajun Bu
    Asian Conference on Computer Vision (ACCV) (2004)
    Parameter optimization for active shape models
    Chun Chen
    Stan Z. Li
    Jiajun Bu
    Asian Conference on Computer Vision (ACCV) (2004)
    TRECVID 2004 Search and Feature Extraction Task by NUS PRIS
    Tat-Seng Chua
    Shi-Yong Neo
    Ke-Ya Li
    Gang Wang
    Rui Shi
    Huaxin Xu
    TRECVID (2004)
    Subspace Analysis and Optimization for AAM Based Face Alignment
    Chun Chen
    Stan Z. Li
    Jiajun Bu
    FGR (2004), pp. 290-295
    Audio and video combined for home video abstraction
    Jiajun Bu
    Chun Chen
    International Conference on Acoustic, Speech and Signal Processing (ICASSP) (2003)
    A statistical inference based automatic video object segmentation algorithm
    Na Li
    Chun Chen
    Journal of Computer-Aided Design and Computer Graphics(Chinese) (2003)
    Robust background subtraction in HSV color space
    Jiajun Bu
    Chun Chen
    SPIE: Multimedia Systems and Applications V (2002)
    Hierarchy optical flow based semi-automatic spatial-temporal video segmentation
    Chun Chen
    Zhengping Wu
    Journal of Image and Graphics(Chinese) (2002)
    Automatic home video abstraction using audio contents
    Caifu Chen
    Chun Chen
    Jjajun Bu
    SPIE: Electronic Imaging and Multimedia Technology III (2002)
    Semi-automatic video object segmentation basing on hierarchy optical flow
    Jiajun Bu
    Chun Chen
    SPIE: Electronic Imaging and Multimedia Technology III (2002)