Efficient Histogram-Based Indexing for Video Copy Detection

Given a source video sequence, a video copy detec- tion technique tries to find sequences whose contents are identical to, or near-duplicates of, the source. In this paper, we mainly focus on the efficiency issue to propose a novel indexing method for video copy detec- tion. Two indexing algorithms, the min-hashing algo- rithm and the histogram pruning algorithm, are inte- grated based on the histogram's characteristics to speed up the conventional sliding window method. We implement the features of the ordinal signature and the SIFT descriptor individually for evaluation. Experi- ment results show that the proposed method can sub- stantially reduce the computation cost and yield an acceptable detection accuracy rate.

[1]  Chu-Song Chen,et al.  A Framework for Handling Spatiotemporal Variations in Video Copy Detection , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[2]  Chu-Song Chen,et al.  Efficient and Effective Video Copy Detection Based on Spatiotemporal Analysis , 2007, Ninth IEEE International Symposium on Multimedia (ISM 2007).

[3]  Olivier Buisson,et al.  Robust voting algorithm based on labels of behavior for video copy detection , 2006, MM '06.

[4]  Olivier Buisson,et al.  Content-based video copy detection in large databases: a local fingerprints statistical similarity search approach , 2005, IEEE International Conference on Image Processing 2005.

[5]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[6]  Xian-Sheng Hua,et al.  Robust video signature based on ordinal measure , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[7]  Kunio Kashino,et al.  A quick search method for audio and video signals based on histogram pruning , 2003, IEEE Trans. Multim..

[8]  C. Schmid,et al.  A performance evaluation of local descriptors , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[9]  Ruud M. Bolle,et al.  Comparison of sequence matching techniques for video copy detection , 2001, IS&T/SPIE Electronic Imaging.

[10]  Edith Cohen,et al.  Finding interesting associations without support pruning , 2000, Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073).

[11]  Justin Zobel,et al.  Detection of video sequences using compact signatures , 2006, TOIS.

[12]  B. Vasudev,et al.  Spatiotemporal sequence matching for efficient video copy detection , 2005, IEEE Transactions on Circuits and Systems for Video Technology.

[13]  Gabriela Csurka,et al.  Visual categorization with bags of keypoints , 2002, eccv 2004.

[14]  K. Sayood Introduction to Data Compression , 1996 .