A sparse representation-based approach for video copy detection

Abstract

Content-based video copy detection becomes an active research field due to requirement of copyright protection, business intelligence, video retrieval, etc. Although it is assumed in the existing methods that reference database consists of original videos, these videos are difficult to be obtained in many practical cases. In this paper, a copy detection method based on sparse representation is proposed to make use of some imperfect prototypes of original videos maintained in the reference database. A query video is represented as a linear combination of all the videos in the database. Then we can determine that whether the query has sibling videos in the database based on distribution of coefficients and find them out based on reconstruction error. The experiments show that even with very limited dimensional feature, this method can achieve high performance.

Key words： video copy detection; near duplicated video; sparse representation

Acknowledgements

The work was supported by the National Key Foundation R&D Projects (Grant No. 2007CB311003), and the Basic Research Foundation of Tsinghua National Laboratory for Information Science and Technology (TNList). The authors would like to thank the authors of the software for generating duplicated video copies and the software for solving {\ell }_{\mathrm{1}} minimization problem which we used in the experiments.

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