An efficient video copy detection method combining vocabulary tree and inverted file
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Frame filtering and path verification for improving video copy detection
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
ACM Transactions on Information Systems (TOIS)
Content-based copy detection through multimodal feature representation and temporal pyramid matching
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Rotation and flipping robust region binary patterns for video copy detection
Journal of Visual Communication and Image Representation
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Content-based video copy detection is very important for copyright protection in view of the growing popularity of video sharing websites, which deals with not only whether a copy occurs in a query video stream but also where the copy is located and where the copy is originated from. While a lot of work has addressed the problem with good performance, less effort has been made to consider the copy detection problem in the case of a continuous query stream, for which precise temporal localization and some complex video transformations like frame insertion and video editing need to be handled. We attempt to attack the problem by presenting a frame fusion based copy detection approach, which converts video copy detection to frame similarity search and frame fusion under a temporal consistency assumption. Our work focuses mainly on the frame fusion stage due to its critical role in copy detection performance. The proposed frame fusion scheme is based on a Viterbi-like algorithm, comprising an online back-tracking strategy with three relaxed constraints. The experimental results show that the proposed approach achieves high localization accuracy in both the query stream and the reference database even when a query video stream undergoes some complex transformations, while achieving comparable performance compared with state-of-the-art copy detection methods.