文章基本信息

标题：Shearlet Based Video Fingerprint for Content-Based Copy Detection
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作者：Fang Yuan ; Lam-Man Po ; Mengyang Liu 等
期刊名称：Journal of Signal and Information Processing
印刷版ISSN：2159-4465
电子版ISSN：2159-4481
出版年度：2016
卷号：07
期号：02
页码：84-97
DOI：10.4236/jsip.2016.72010
语种：English
出版社：Scientific Research Publishing
摘要：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.
关键词：Video Fingerprint;Content-based Copy Detection;Shearlet Transform