The pyramid-technique: towards breaking the curse of dimensionality
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Key-frame extraction and shot retrieval using nearest feature line (NFL)
MULTIMEDIA '00 Proceedings of the 2000 ACM workshops on Multimedia
Content-based video similarity model
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Efficient video indexing scheme for content-based retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Validating cardiac echo diagnosis through video similarity
Proceedings of the 13th annual ACM international conference on Multimedia
Detection of video sequences using compact signatures
ACM Transactions on Information Systems (TOIS)
Near-duplicate keyframe retrieval by nonrigid image matching
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Learning to rank videos personally using multiple clues
Proceedings of the ACM International Conference on Image and Video Retrieval
The state of the art in image and video retrieval
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Near-duplicate keyframe retrieval by semi-supervised learning and nonrigid image matching
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Fast and robust short video clip search for copy detection
PCM'04 Proceedings of the 5th Pacific Rim Conference on Advances in Multimedia Information Processing - Volume Part II
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In this paper, a new two-phase scheme for video similarity detection is proposed. For each video sequence, we extract two kinds of signatures with different granularities: coarse and fine. Coarse signature is based on the Pyramid Density Histogram (PDH) technique and fine signature is based on the Nearest Feature Trajectory (NFT) technique. In the first phase, most of unrelated video data are filtered out with respect to the similarity measure of the coarse signature. In the second phase, the query video example is compared with the results of the first phase according to the similarity measure of the fine signature. Different from the conventional nearest neighbor comparison, our NFT based similarity measurement method well incorporates the temporal order of video sequences. Experimental results show that our scheme achieves better quality results than the conventional approach.