JSIP  Vol.7 No.2 , May 2016
Shearlet Based Video Fingerprint for Content-Based Copy Detection
Abstract: Content-based copy detection (CBCD) is widely used in copyright control for protecting unauthorized use of digital video and its key issue is to extract robust fingerprint against different attacked versions of the same video. In this paper, the “natural parts” (coarse scales) of the Shearlet coefficients are used to generate robust video fingerprints for content-based video copy detection applications. The proposed Shearlet-based video fingerprint (SBVF) is constructed by the Shearlet coefficients in Scale 1 (lowest coarse scale) for revealing the spatial features and Scale 2 (second lowest coarse scale) for revealing the directional features. To achieve spatiotemporal natural, the proposed SBVF is applied to Temporal Informative Representative Image (TIRI) of the video sequences for final fingerprints generation. A TIRI-SBVF based CBCD system is constructed with use of Invert Index File (IIF) hash searching approach for performance evaluation and comparison using TRECVID 2010 dataset. Common attacks are imposed in the queries such as luminance attacks (luminance change, salt and pepper noise, Gaussian noise, text insertion); geometry attacks (letter box and rotation); and temporal attacks (dropping frame, time shifting). The experimental results demonstrate that the proposed TIRI-SBVF fingerprinting algorithm is robust on CBCD applications on most of the attacks. It can achieve an average F1 score of about 0.99, less than 0.01% of false positive rate (FPR) and 97% accuracy of localization.
Cite this paper: Yuan, F. , Po, L. , Liu, M. , Xu, X. , Jian, W. , Wong, K. and Cheung, K. (2016) Shearlet Based Video Fingerprint for Content-Based Copy Detection. Journal of Signal and Information Processing, 7, 84-97. doi: 10.4236/jsip.2016.72010.

[1]   YouTube Statistics, YouTube.

[2]   Lu, J. (2009)Video Fingerprinting for Copy Identification: From Research to Industry Applications. Proceedings of SPIE, Media Forensics and Security.

[3]   Hampapur, A. and Bolle, R.M. (2001) Comparison of Distance Measures for Video Copy Detection. ICME 2001, IEEE International Conference on Multimedia and Expo 2001, Tokyo, 22-25 August 2001, 737-740.

[4]   Hampapur, A., Hyun, K. and Bolle, R.M. (2002) Comparison of Sequence Matching Techniques for Video Copy Detection, Proceeding of SPIE 4676, Storage and Retrieval for Media Databased, 4676, 194-201.

[5]   Chen, L. and Stentiford, F. (2008) Video Sequence Matching Based on Temporal Ordinal Measurement. Pattern Recognition Letters, 29, 1824-1831.

[6]   Li, T., Nian, F., Wu, X., Gao, Q. and Lu, Y. (2014) Efficient Video Copy Detection Using Multi-modality and Dynamic Path Search. Multimedia System, 22, 29-39.

[7]   Radhakrishnan, R. and Bauer, C. (2007) Content-Based Video Signatures Based on Projections of Difference Images, IEEE 9th Workshop on Multimedia Signal Processing, Crete, 1-3 October 2007, 341-344.

[8]   De Roover, C., De Vleeschouwer, C. and Macq, B. (2005) Robust Video Hashing Based on Radial Projections of Key Frames. IEEE Transactions on Signal Processing, 53, 4020-4037.

[9]   Kim, C. and Vasudev, B. (2005) Spatiotemporal Sequence Matching for Efficient Video Copy Detection, IEEE Transactions on Circuits and Systems for Video Technology, 15, 127-132.

[10]   Esmaeili, M.M., Fatourechi, M., and Ward, R.K. (2011) A Robust and Fast Video Copy Detection System Using Content-Based Fingerprinting, IEEE Transactions on Information Forensics and Security, 6, 213-226.

[11]   Esmaeili, M.M., Fatourechi, M. and Ward, R.K. (2009) Video Copy Detection Using Temporally Informative Representative Images. International Conference on Machine Learning and Applications (ICMLA), Miami Beach, December 2009, 69-74.

[12]   Li, Y., Po, L.M., Xu, X., Feng, L. and Yuan, F. (2015) No-Reference Image Quality Assessment with Shearlet Transform and Deep Neural Networks. Neurocomputing, 154, 94-109.

[13]   Li, Y., Po, L.M., Cheung, C.H., Xu, X., Feng, L. and Yuan, F. (2015) No-Reference Video Quality Assessment with 3D Shearlet Transform and Convolutional Neural Networks. IEEE Transactions on Circuits and Systems for Video Technology, 1.

[14]   Yi, S., Labate, D., Easley, G.R. and Krim, H. (2009) A Shearlet Approach to Edge Analysis and Detection. IEEE Transactions on Image Processing, 18, 929-941.

[15]   Kutyniok, G. and Lim, W. (2010) Image Separation Using Wavelets and Shearlets. Curves and Surfaces, 6920, 416-430.

[16]   Kutyniok, G., Shahram, M. and Zhuang, X. (2011) ShearLab: A Rational Design of A Digital Parabolic Scaling Algorithm. arXiv: 1106.1319v1.

[17]   Kutyniok, G., Lim, W. and Zhuang, X. (2012) Digital Shearlet Transforms. In: Kutyniok, G. and Labate, D., Eds., Shearlets, Birkhauser, Boston, 239-282.

[18]   Lim, W.-Q. (2010) The Discrete Shearlet Transform: A New Directional Transform and Compactly Supported Shearlet Frames. IEEE Transactions on Image Processing, 19, 1166-1180.

[19]   ShearLab.

[20]   Zhou, J.P., Cunha, A.L. and Do, M.N. (2005) Nonsubsampled Contourlet Transform Construction and Application Enhancement. IEEE International Conference on Image Processing, 1, 469-472.

[21]   Easley, G., Labate, D. and Lim, W.-Q. (2008) Sparse Directional Image Representations Using the Discrete Shearlet Transform. Applied and Computational Harmonic Analysis, 25, 25-46.

[22]   Gupta, V., Boulianne, G. and Cardinal, P. (2012) CRIM’s Content-Based Audio Copy Detection System for TRECVID 2009. Multimedia Tools and Applications, 60, 371-387.

[23]   Smeaton, A.F., Kraaij, W. and Over, P. (2004) The TREC Video Retrieval Evaluation (TRECVID): A Case Study and Status Report. RIAO 2004, International Conference of Computer-Assisted Information Retrieval, Avignon, 26-28 April 2004, 25-37.

[24]   Jegou, H., Douze, M. and Schmid, C. (2008) Hamming Embedding and Weak Geometry Consistency for Large Scale Image Search. The 10th European Conference on Computer Vision, Marseille, 12-18 October 2008, 304-317.

[25]   Sivic, J. and Zisserman, A. (2003) Video Google: A Text Retrieval Approach to Object Matching in Videos. The 9th IEEE International Conference on Computer Vision, Nice, 13-16 October 2003, 1470-1477.

[26]   Douze, M., Jegou, H. and Schmid, C. (2010) An Image-Based Approach to Video Copy Detection with Spatio-Temporal Post-Filtering. IEEE Transactions on Multimedia, 12, 257-266.

[27]   Oostveen, J., Kalker, T. and Haitsma, J. (2002) Feature Extraction and a Database Strategy for Video Fingerprinting. 5th International Conference, VISUAL 2002, Hsin Chu, 11-13 March 2002, 117-128.

[28]   Haitsma, J. and Kalker, T. (2003) A Highly Robust Audio Fingerprinting System with an Efficient Search Strategy. Journal of New Music Research, 32, 211-222.

[29]   Law-To, J., Chen, L., Alexis, J., Ivan, L., Olivier, B., Valerie, G.B., Nozha, B. and Fred, S. (2007) Video Copy Detection: A Comparative Study. Proceedings of the 6th ACM International Conference on Image and Video Retrieval, Amsterdam, 9-11 July 2007, 371-378.