A content-based video copy detection method with randomly projected binary features

Video copy detection has been actively studied in a wide range of multimedia applications. This paper presents a novel content-based video copy detection method using the randomly projected binary features. A very efficient sparse random projection method is employed to encode the image features while retaining their discrimination capability. By taking advantage of the extremely fast similarity computation of binary features using Hamming distance, we present a keyframe-based copy retrieval method that exhaustively searches the copy candidates from the large video database without indexing. Moreover, an effective scoring and localization algorithm is proposed to further refine the retrieved copies and accurately locate the video segments. The experimental evaluation has been performed to show the efficacy of the proposed randomly projected binary features. The promising results in the TRECVID2011 [14] content-based copy detection task demonstrated the effectiveness of our proposed approach.

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

[2]  Wen Gao,et al.  PKU-IDM @TRECVID2011 CBCD: Content-Based Copy Detection with Cascade of Multimodal Features and Temporal Pyramid Matching , 2011, TRECVID.

[3]  Benjamin Bustos,et al.  Combining Features at Search Time: PRISMA at Video Copy Detection Task , 2011, TRECVID.

[4]  Shuicheng Yan,et al.  Near-duplicate keyframe retrieval by semi-supervised learning and nonrigid image matching , 2011, TOMCCAP.

[5]  Paul Over,et al.  TRECVID 2008 - Goals, Tasks, Data, Evaluation Mechanisms and Metrics , 2010, TRECVID.

[6]  Chun Chen,et al.  Semi-Supervised Nonlinear Hashing Using Bootstrap Sequential Projection Learning , 2013, IEEE Transactions on Knowledge and Data Engineering.

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

[8]  Antonio Torralba,et al.  Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope , 2001, International Journal of Computer Vision.

[9]  Aggelos K. Katsaggelos,et al.  Local feature extraction for video copy detection in a database , 2008, 2008 15th IEEE International Conference on Image Processing.

[10]  Zi Huang,et al.  Multiple feature hashing for real-time large scale near-duplicate video retrieval , 2011, ACM Multimedia.

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

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

[13]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[14]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

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

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

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

[18]  Antonio Torralba,et al.  Spectral Hashing , 2008, NIPS.

[19]  Kenneth Ward Church,et al.  Very sparse random projections , 2006, KDD '06.

[20]  Yusuke Uchida,et al.  KDDI LABS at TRECVID 2011: Content-Based Copy Detection , 2011, TRECVID.

[21]  Andrew Zisserman,et al.  Representing shape with a spatial pyramid kernel , 2007, CIVR '07.

[22]  Benjamin Bustos,et al.  Content-Based Video Copy Detection: PRISMA at TRECVID 2010 , 2010, TRECVID.