Robust video identification approach based on local non-negative matrix factorization

With the popularization of media-capture devices and the development of the Internet's basic facilities, video has become the most popular media information in recent years. The massive capacity of video imposes the demand of automatic video identification techniques which are very important to various applications such as content based video retrieval and copy detection. Therefore, as a challenging problem, video identification has drawn more and more attention in the past decade. The problem addressed here is to identify a given video clip in a given set of video sequences. In this paper, a robust video identification algorithm based on local non-negative matrix factorization (LNMF) is presented. First, some concepts about LNMF are described and the way of finding the factorized matrix is given. Then, its convergence is proven. In addition, a LNMF based shot detection method is proposed for constructing a video identification framework completely based on LNMF. Finally, a LNMF based identification approach using Hausdorff distance is introduced and a two-stage search process is proposed. Experimental results show the robustness of the proposed approach to many kinds of content-preserved distortions and its superiority to other algorithms.