Design and deployment of a digital forensics service platform for online videos
MiFor '09 Proceedings of the First ACM workshop on Multimedia in forensics
IVForensic: a digital forensics service platform for internet videos
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Robust video fingerprinting based on symmetric pairwise boosting
IEEE Transactions on Circuits and Systems for Video Technology
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Video fingerprinting based on orientation of luminance centroid
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Quantum hashing for multimedia
IEEE Transactions on Information Forensics and Security - Special issue on electronic voting
Distance metric learning for content identification
IEEE Transactions on Information Forensics and Security
Robust video fingerprinting based on hierarchical symmetric difference feature
Proceedings of the 20th ACM international conference on Information and knowledge management
Fusing audio-visual fingerprint to detect TV commercial advertisement
Computers and Electrical Engineering
Video fingerprinting using Latent Dirichlet Allocation and facial images
Pattern Recognition
Robust 3D mesh model hashing based on feature object
Digital Signal Processing
Frontiers of Computer Science: Selected Publications from Chinese Universities
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Video fingerprints are feature vectors that uniquely characterize one video clip from another. The goal of video fingerprinting is to identify a given video query in a database (DB) by measuring the distance between the query fingerprint and the fingerprints in the DB. The performance of a video fingerprinting system, which is usually measured in terms of pairwise independence and robustness, is directly related to the fingerprint that the system uses. In this paper, a novel video fingerprinting method based on the centroid of gradient orientations is proposed. The centroid of gradient orientations is chosen due to its pairwise independence and robustness against common video processing steps that include lossy compression, resizing, frame rate change, etc. A threshold used to reliably determine a fingerprint match is theoretically derived by modeling the proposed fingerprint as a stationary ergodic process, and the validity of the model is experimentally verified. The performance of the proposed fingerprint is experimentally evaluated and compared with that of other widely-used features. The experimental results show that the proposed fingerprint outperforms the considered features in the context of video fingerprinting.