A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
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Digital watermarking
Feature Extraction and a Database Strategy for Video Fingerprinting
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Pattern Recognition and Machine Learning (Information Science and Statistics)
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IEEE Transactions on Circuits and Systems for Video Technology
Robust Video Fingerprinting for Content-Based Video Identification
IEEE Transactions on Circuits and Systems for Video Technology
Video fingerprinting using Latent Dirichlet Allocation and facial images
Pattern Recognition
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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 conductive 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.