TITLE:
Shearlet Based Video Fingerprint for Content-Based Copy Detection
AUTHORS:
Fang Yuan, Lam-Man Po, Mengyang Liu, Xuyuan Xu, Weihua Jian, Kaman Wong, Keith W. Cheung
KEYWORDS:
Video Fingerprint, Content-based Copy Detection, Shearlet Transform
JOURNAL NAME:
Journal of Signal and Information Processing,
Vol.7 No.2,
May
17,
2016
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.