Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Multimodal Fusion for Video Search Reranking
IEEE Transactions on Knowledge and Data Engineering
Compact video description for copy detection with precise temporal alignment
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Product Quantization for Nearest Neighbor Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Image-Based Approach to Video Copy Detection With Spatio-Temporal Post-Filtering
IEEE Transactions on Multimedia
Spatiotemporal sequence matching for efficient video copy detection
IEEE Transactions on Circuits and Systems for Video Technology
Frame Fusion for Video Copy Detection
IEEE Transactions on Circuits and Systems for Video Technology
Efficient video copy detection via aligning video signature time series
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
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Recently, the frame fusion based video copy detection scheme provides a possibility to detect copies in a continuous query video stream. However, its computational complexity is high since a large amount of returned reference frames need to be handled by some reference clip reconstruction methods. In addition, dense frame sampling strategies generally used for improving copy localization precision not only further aggravates the computational efficiency but also leads to much more false alarms due to content redundancy among frames. To alleviate the above problems, a new scheme is proposed for improving the performance of the frame fusion based video copy detection in both efficiency and effectiveness. In particular, the continuous similarity property among neighbor frames is learned for guiding the design of smart frame filtering method so as to greatly reduce the redundancy among frames. Then, an effective path verification scheme, which utilizes cross-clip verification strategy, is given for removing false alarms. The extensive experimental results show that the proposed schemes remarkably improve the detection accuracy of the baseline frame fusion scheme and give a comparable localization accuracy to it.