Efficient Mining of Multiple Partial Near-Duplicate Alignments by Temporal Network

This paper considers the mining and localization of near-duplicate segments at arbitrary positions of partial near-duplicate videos in a corpus. Temporal network is proposed to model the visual-temporal consistency between video sequence by embedding temporal constraints as directed edges in the network. Partial alignment is then achieved through network flow programming. To handle multiple alignments, we consider two properties of network structure: conciseness and divisibility, to ensure that the mining is efficient and effective. Frame-level matching is further integrated in the temporal network for alignment verification. This results in an iterative alignment-verification procedure to fine tune the localization of near-duplicate segments. The scalability of frame-level matching is enhanced by exploring visual keyword matching algorithms. We demonstrate the proposed work for mining partial alignments from two months of broadcast videos and across six TV sources.

[1]  Hung-Khoon Tan,et al.  Scalable detection of partial near-duplicate videos by visual-temporal consistency , 2009, ACM Multimedia.

[2]  Zhe Wang,et al.  Efficiently matching sets of features with random histograms , 2008, ACM Multimedia.

[3]  Cordelia Schmid,et al.  INRIA-LEAR'S Video Copy Detection System , 2008, TRECVID.

[4]  Shuicheng Yan,et al.  Near-duplicate keyframe retrieval by nonrigid image matching , 2008, ACM Multimedia.

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

[6]  Chu-Song Chen,et al.  A Time Warping Based Approach for Video Copy Detection , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[7]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[8]  Jun Adachi,et al.  Scene duplicate detection based on the pattern of discontinuities in feature point trajectories , 2008, ACM Multimedia.

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

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

[12]  Qi Tian,et al.  The use of temporal, semantic and visual partitioning model for efficient near-duplicate keyframe detection in large scale news corpus , 2007, CIVR '07.

[13]  Cordelia Schmid,et al.  An Image-Based Approach to Video Copy Detection With Spatio-Temporal Post-Filtering , 2010, IEEE Transactions on Multimedia.

[14]  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.

[15]  Hung-Khoon Tan,et al.  Event driven summarization for web videos , 2009, WSM '09.

[16]  Shih-Fu Chang,et al.  Video search reranking through random walk over document-level context graph , 2007, ACM Multimedia.

[17]  W. Marsden.  I and J , 2012 .

[18]  Paul Over,et al.  Evaluation campaigns and TRECVid , 2006, MIR '06.

[19]  Yan Ke,et al.  Efficient Near-duplicate Detection and Sub-image Retrieval , 2004 .

[20]  Shih-Fu Chang,et al.  Internet image archaeology: automatically tracing the manipulation history of photographs on the web , 2008, ACM Multimedia.

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

[22]  Chong-Wah Ngo,et al.  Scale-Rotation Invariant Pattern Entropy for Keypoint-Based Near-Duplicate Detection , 2009, IEEE Transactions on Image Processing.

[23]  Yan Ke,et al.  An efficient parts-based near-duplicate and sub-image retrieval system , 2004, MULTIMEDIA '04.

[24]  Michael Isard,et al.  General Theory , 1969 .

[25]  Hung-Khoon Tan,et al.  Near-Duplicate Keyframe Identification With Interest Point Matching and Pattern Learning , 2007, IEEE Transactions on Multimedia.

[26]  Fred Stentiford,et al.  Video sequence matching based on temporal ordinal measurement , 2008, Pattern Recognit. Lett..

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

[28]  Olivier Buisson,et al.  Scalable mining of large video databases using copy detection , 2008, ACM Multimedia.

[29]  NgoChong-Wah,et al.  Near-Duplicate Keyframe Identification With Interest Point Matching and Pattern Learning , 2007 .

[30]  Jon Louis Bentley,et al.  An Algorithm for Finding Best Matches in Logarithmic Expected Time , 1977, TOMS.

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

[32]  Cordelia Schmid,et al.  An Affine Invariant Interest Point Detector , 2002, ECCV.

[33]  David K. Smith.  Network Flows: Theory, Algorithms, and Applications , 1994 .

[34]  Shih-Fu Chang,et al.  Detecting image near-duplicate by stochastic attributed relational graph matching with learning , 2004, MULTIMEDIA '04.

[35]  Nuria Oliver,et al.  Understanding near-duplicate videos: a user-centric approach , 2009, ACM Multimedia.

[36]  Mark Sanderson,et al.  Automatic video tagging using content redundancy , 2009, SIGIR.

[37]  Andrew Zisserman,et al.  Video Google: a text retrieval approach to object matching in videos , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.