Author(s)

Fang Yuan¹, Lam-Man Po¹, Mengyang Liu¹, Xuyuan Xu², Weihua Jian², Kaman Wong², Keith W. Cheung³

¹Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China.
²Video Team, Tencent Holdings Limited, Shenzhen, China.
³School of Communication, Hang Seng Management College, Hong Kong, China.

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.

Video Fingerprint, Content-based Copy Detection, Shearlet Transform

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.