A sparse representation-based approach for video copy detection

Content-based video copy detection becomes an active research field due to requirement of copyright protection, business intelligence, video retrieval, etc. Although it is assumed in the existing methods that reference database consists of original videos, these videos are difficult to be obtained in many practical cases. In this paper, a copy detection method based on sparse representation is proposed to make use of some imperfect prototypes of original videos maintained in the reference database. A query video is represented as a linear combination of all the videos in the database. Then we can determine that whether the query has sibling videos in the database based on distribution of coefficients and find them out based on reconstruction error. The experiments show that even with very limited dimensional feature, this method can achieve high performance.

[1]  Olivier Buisson,et al.  Robust Content-Based Video Copy Identification in a Large Reference Database , 2003, CIVR.

[2]  Cordelia Schmid,et al.  Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search , 2008, ECCV.

[3]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

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

[5]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[6]  Mark D. Plumbley.  Recovery of Sparse Representations by Polytope Faces Pursuit , 2006, ICA.

[7]  Junfeng Yang,et al.  Alternating Direction Algorithms for 1-Problems in Compressive Sensing , 2009, SIAM J. Sci. Comput..

[8]  Chong-Wah Ngo,et al.  Practical elimination of near-duplicates from web video search , 2007, ACM Multimedia.

[9]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Nasir D. Memon,et al.  Spatio–Temporal Transform Based Video Hashing , 2006, IEEE Transactions on Multimedia.

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

[12]  Sunil Arya,et al.  An optimal algorithm for approximate nearest neighbor searching fixed dimensions , 1998, JACM.

[13]  Mário A. T. Figueiredo,et al.  Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and Other Inverse Problems , 2007, IEEE Journal of Selected Topics in Signal Processing.

[14]  Stephen J. Wright,et al.  Sparse reconstruction by separable approximation , 2009, IEEE Trans. Signal Process..

[15]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[16]  Sid-Ahmed Berrani,et al.  A probabilistic framework for fusing frame-based searches within a video copy detection system , 2008, CIVR '08.

[17]  Nicole Immorlica,et al.  Locality-sensitive hashing scheme based on p-stable distributions , 2004, SCG '04.

[18]  S. Mallat,et al.  Adaptive greedy approximations , 1997 .

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

[20]  Lu Liu,et al.  Video Histogram: A Novel Video Signature for Efficient Web Video Duplicate Detection , 2007, MMM.

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

[22]  M. R. Osborne,et al.  A new approach to variable selection in least squares problems , 2000 .

[23]  Li Chen,et al.  Video copy detection: a comparative study , 2007, CIVR '07.