Ming Zhao

Ming Zhao

Authored Publications
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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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    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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    Taxonomic Classification for Web-based Videos
    Xiaoyun Wu
    IEEE Conf on Computer Vision and Pattern Recognition (CVPR), IEEE (2010)
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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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    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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    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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