Robust Video Fingerprinting Based on Symmetric Pairwise Boosting

Abstract

This paper proposes a video fingerprinting method based on a novel binary fingerprint obtained using a feature selection algorithm called the symmetric pairwise boosting (SPB). The binary fingerprints are obtained by filtering and quantizing perceptually significant features extracted from an input video clip. The SPB algorithm, which is a generalization of the conventional asymmetric pairwise boosting (APB), selects appropriate filters and quantizers from a class of candidate filters and quantizers in such a way that perceptually similar and dissimilar pairs of video clips are correctly classified as matching and non-matching pairs, respectively. The binary form of the novel fingerprint makes it conducive to an efficient database search, and the experimental results show that the proposed method outperforms the APB-based video fingerprinting methods in terms of both robustness and discriminability.