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Spatial Pyramid Covariance-Based Compact Video Code for Robust Face Retrieval in TV-Series.

Yan Li, Ruiping Wang, Zhen Cui

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
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    This study introduces a compact video code (CVC) for efficient face video retrieval in TV series. The CVC method effectively represents face tracks, improving retrieval accuracy and speed for challenging video content.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Face video retrieval in TV series is challenging due to uncontrolled conditions and appearance variations.
    • Existing retrieval methods require efficient representations with low time and space complexity.

    Purpose of the Study:

    • To propose a compact and discriminative representation for face video data to enable efficient retrieval.
    • To develop a novel hierarchical video representation for improved face track modeling.

    Main Methods:

    • Modeling face tracks using sample covariance matrices for statistical variation capture.
    • Encoding covariance representations into low-dimensional binary vectors (Compact Video Code - CVC) using a max-margin framework.
    • Developing a spatial pyramid covariance representation with a fast calculation method for hierarchical video representation.

    Main Results:

    • The proposed CVC method demonstrates competitiveness against state-of-the-art retrieval methods on TV series databases (Big Bang Theory, Prison Break).
    • CVC achieves promising performance in traditional video face recognition on the YouTube Celebrities dataset using a compact 128-bit code.

    Conclusions:

    • Compact Video Code (CVC) offers an efficient and discriminative solution for face video retrieval in challenging TV series.
    • The CVC method shows potential as a general video matching algorithm with excellent performance and compact representation.