Ordinal Measures for Image Correspondence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust voting algorithm based on labels of behavior for video copy detection
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Video sequence matching based on temporal ordinal measurement
Pattern Recognition Letters
Robust copy detection by mining temporal self-similarities
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
A Simple but Effective Approach to Video Copy Detection
CRV '10 Proceedings of the 2010 Canadian Conference on Computer and Robot Vision
Motion Vector Based Features for Content Based Video Copy Detection
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
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
Spider: A system for finding 3D video copies
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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Video copy detection algorithms are used to find copies of original video content even if the content has been altered. Given the prevalence of video recording and copying devices as well as the availability of many Internet sites for hosting videos, detecting video copies has become an important problem especially for companies interested in managing and controlling copyrights of their content. We propose a new content-based video copy detection algorithm. The proposed algorithm creates signatures that capture the spatial and temporal features of videos. These spatio-temporal signatures enable the algorithm to provide both high precision and recall. In addition, these signatures require small storage and are easy to compute and compare. Our extensive experimental analysis with a large video dataset shows that the proposed algorithm achieves high precision and recall values while remaining robust to many video transformations that commonly occur in practice. The algorithm is simple to implement and is more computationally efficient than previous algorithms in literature.