Compact Video Code and Its Application to Robust Face Retrieval in TV-Series
In Proceedings British Machine Vision Conference 2014
AbstractWe address the problem of video face retrieval in TV-Series which searches video clips based on the presence of specific character, given one video clip of him/her. This is tremendously challenging because on one hand, faces in TV-Series are largely uncontrolled with complex appearance variations, and on the other hand retrieval task typically needs efficient representation for both time fast and space saving search. To solve this problem, we propose a compact and discriminative representation for the huge body of video data, named Compact Video Code (CVC). Our method first models the video clip by its sample (i.e., frame) covariance matrix to capture the video data variations in a statistical manner. To incorporate discriminative information and obtain more compact video signature, the high-dimensional covariance matrix is further encoded as a much lower-dimensional binary vector, which finally yields the proposed CVC. Specifically, each bit of the code, i.e., each dimension of the binary vector, is produced via supervised learning in a max margin framework, which aims to make a balance between the discriminability and stability of the code. Face retrieval experiments on two challenging TV-Series video databases have demonstrated the advantage of the proposed CVC over state-of-the-art retrieval methods. In addition, as a general video matching algorithm, our method is also evaluated in traditional video face recognition task on a standard Internet database, i.e., YouTube Celebrities, showing its quite promising performance by using an extremely compact code with only 128 bits.
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