A Multiple Features Video Copy Detection Algorithm Based on a SURF Descriptor

Considering video copy transform diversity, a multi-feature video copy detection algorithm based on a Speeded-Up Robust Features (SURF) local descriptor is proposed in this paper. Video copy coarse detection is done by an ordinal measure (OM) algorithm after the video is preprocessed. If the matching result is greater than the specified threshold, the video copy fine detection is done based on a SURF descriptor and a box filter is used to extract integral video. In order to improve video copy detection speed, the Hessian matrix trace of the SURF descriptor is used to pre-match, and dimension reduction is done to the traditional SURF feature vector for video matching. Our experimental results indicate that video copy detection precision and recall are greatly improved compared with traditional algorithms, and that our proposed multiple features algorithm has good robustness and discrimination accuracy, as it demonstrated that video detection speed was also improved.

[1]  Cordelia Schmid,et al.  A Performance Evaluation of Local Descriptors , 2005, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Olivier Buisson,et al.  Content-Based Copy Retrieval Using Distortion-Based Probabilistic Similarity Search , 2007, IEEE Transactions on Multimedia.

[3]  Shuicheng Yan,et al.  Detecting Anomaly in Videos from Trajectory Similarity Analysis , 2007, 2007 IEEE International Conference on Multimedia and Expo.

[4]  Chang Dong Yoo,et al.  Robust video fingerprinting for content-based video identification , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[5]  Hong Liu,et al.  SVD-SIFT for web near-duplicate image detection , 2010, 2010 IEEE International Conference on Image Processing.

[6]  Jean-Philippe Thiran,et al.  Scale Invariant Feature Transform on the Sphere: Theory and Applications , 2011, International Journal of Computer Vision.

[7]  Kristen Grauman,et al.  Kernelized Locality-Sensitive Hashing , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Xinbo Gao,et al.  Learning to multimodal hash for robust video copy detection , 2013, 2013 IEEE International Conference on Image Processing.

[9]  Xavier Anguera Miró,et al.  Speed improvements to Information Retrieval-based dynamic time warping using hierarchical K-Means clustering , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[10]  Gozde Bozdagi Akar,et al.  Visual Group Binary Signature for Video Copy Detection , 2014, 2014 22nd International Conference on Pattern Recognition.

[11]  Sanket Shinde,et al.  Recent advances in content based video copy detection , 2015, 2015 International Conference on Pervasive Computing (ICPC).