Abstract
Video copy detection techniques are essential for a number of applications including discovering copyright infringement of multimedia content, monitoring commercial air time, and querying videos by example. Over the last decade, video copy detection has received rapidly growing attention from the multimedia research community. To encourage more innovative technology and benchmark the state of the art approaches in this field, the TRECVID conference series, sponsored by the NIST, initiated an evaluation task on content based copy detection in 2008. In this paper, we describe the content-based video copy detection framework developed at AT&T Labs - Research. We employed local visual features to match the video content, and adopted locality sensitve hashing and random sample consensus techniques to maintain the scalability and the robustness of our approach. Experimental results on TRECVID 2008 data show that our approach is effective and efficient.
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