Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Generalized RANSAC Framework for Relaxed Correspondence Problems
3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
Video copy detection: a comparative study
Proceedings of the 6th ACM international conference on Image and video retrieval
Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Compressed Domain Copy Detection of Scalable SVC Videos
CBMI '09 Proceedings of the 2009 Seventh International Workshop on Content-Based Multimedia Indexing
Large scale content analysis engine
LS-MMRM '09 Proceedings of the First ACM workshop on Large-scale multimedia retrieval and mining
Video sequence identification in TV broadcasts
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part I
Proceedings of the 3rd Multimedia Systems Conference
Security-oriented picture-in-picture visual modifications
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Spider: A system for finding 3D video copies
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Similar video detection using multiple direct-mapped cache
International Journal of Intelligent Systems Technologies and Applications
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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.