Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Content Based Copy Detection with Coarse Audio-Visual Fingerprints
CBMI '09 Proceedings of the 2009 Seventh International Workshop on Content-Based Multimedia Indexing
Content-Based Copy Retrieval Using Distortion-Based Probabilistic Similarity Search
IEEE Transactions on Multimedia
IEEE Transactions on Circuits and Systems for Video Technology
Rotation and flipping robust region binary patterns for video copy detection
Journal of Visual Communication and Image Representation
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Content-based video copy detection algorithms (CBCD) focus on detecting video segments that are identical or transformed versions of segments in a known video. In recent years some systems have proposed the combination of orthogonal modalities (e.g. derived from audio and video) to improve detection performance, although not always achieving consistent results. In this paper we propose a fusion algorithm that is able to combine as many modalities as available at the decision level. The algorithm is based on the weighted sum of the normalized scores, which are modified depending on how well they rank in each modality. This leads to a virtually parameter-free fusion algorithm. We performed several tests using 2010 TRECVID VCD datasets and obtain up to 46% relative improvement in min-NDCR while also improving the F1 metric on the fused results in comparison to just using the best single modality.