Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Practical elimination of near-duplicates from web video search
Proceedings of the 15th international conference on Multimedia
Scene duplicate detection based on the pattern of discontinuities in feature point trajectories
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Efficiently matching sets of features with random histograms
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Scalable detection of partial near-duplicate videos by visual-temporal consistency
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Real-time near-duplicate elimination for web video search with content and context
IEEE Transactions on Multimedia - Special issue on integration of context and content
An efficient near-duplicate video shot detection method using shot-based interest points
IEEE Transactions on Multimedia
Scale-rotation invariant pattern entropy for keypoint-based near-duplicate detection
IEEE Transactions on Image Processing
Improved Keypoint Matching Method for Near-Duplicate Keyframe Retrieval
ISM '09 Proceedings of the 2009 11th IEEE International Symposium on Multimedia
Practical Online Near-Duplicate Subsequence Detection for Continuous Video Streams
IEEE Transactions on Multimedia
Efficient video similarity measurement with video signature
IEEE Transactions on Circuits and Systems for Video Technology
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We propose a novel video signature called scene signature which is defined as a collection of SIFT descriptors. A scene signature represents the visual cues from a video scene in a compact and comprehensive manner. We detect Near Duplicate Keyframe clusters within a news story and then for each of them we generate an initial scene signature including most informative mutual and distinctive visual cues. Compared to conventional keypoint-trajectory-based signatures, we take the co-occurrence of SIFT keypoints into account. Moreover, we utilize keypoints describing novel visual clues in the scene. Next, through three steps of refinements on the initial scene signature we shorten the semantic gap to obtain more compact and semantically meaningful scene signatures. The experimental results confirm the efficiency, robustness and uniqueness of our proposed scene signature compared to other global and local video signatures.