A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Extraction and a Database Strategy for Video Fingerprinting
VISUAL '02 Proceedings of the 5th International Conference on Recent Advances in Visual Information Systems
Robust voting algorithm based on labels of behavior for video copy detection
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Video sequence matching based on temporal ordinal measurement
Pattern Recognition Letters
Spatio–Temporal Transform Based Video Hashing
IEEE Transactions on Multimedia
Robust Video Fingerprinting for Content-Based Video Identification
IEEE Transactions on Circuits and Systems for Video Technology
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Ideal video fingerprinting should be robust to various practical distortions. Conventional fingerprinting mainly copes with natural distortions (brightness change, resolution reduction, etc.), while always gives poor performance in case of text insertion. One alterative way is to apply a weighting scheme based on the probability of text insertion for feature similarity calculation. However, the weights must be learned with labeled samples. In this paper, we propose a method that first addresses valid regions where the saliency values keep consistent between the query and original frames, namely saliency-consistent regions. Other regions, probably the inserted ones, are discarded. Then a DCT-based hamming distance is calculated on those saliency-consistent regions. Besides, the saliency-based distance is also considered and a further weighted linear distance is evaluated. The proposed algorithm is tested on the MPEG-7 video fingerprint dataset, achieving a false rate of 0.7% in case of text insertion and 0.32% in average for other 8 distortions.