VideoQ: an automated content based video search system using visual cues
MULTIMEDIA '97 Proceedings of the fifth ACM international conference on Multimedia
Introduction to algorithms
Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Towards auto-documentary: tracking the evolution of news stories
Proceedings of the 12th annual ACM international conference on Multimedia
Comparison of Similarity Measures for Trajectory Clustering in Outdoor Surveillance Scenes
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
A framework for multimedia content abstraction and its application to rushes exploration
Proceedings of the 6th ACM international conference on Image and video retrieval
The trecvid 2007 BBC rushes summarization evaluation pilot
Proceedings of the international workshop on TRECVID video summarization
Skimming rushes video using retake detection
Proceedings of the international workshop on TRECVID video summarization
IEEE Transactions on Multimedia
Organizing rushes video by visually similar setting
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Video redundancy detection in rushes collection
TVS '08 Proceedings of the 2nd ACM TRECVid Video Summarization Workshop
Comparison of content selection methods for skimming rushes video
TVS '08 Proceedings of the 2nd ACM TRECVid Video Summarization Workshop
Rushes video summarization using a collaborative approach
TVS '08 Proceedings of the 2nd ACM TRECVid Video Summarization Workshop
A Novel Retake Detection Using LCS and SIFT Algorithm
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Evaluating detection of near duplicate video segments
Proceedings of the ACM International Conference on Image and Video Retrieval
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In applications such as video post-production users are confronted with large amounts of redundant unedited raw material, called rushes. Viewing and organizing this material are crucial but time consuming tasks. Typically multiple but slightly different takes of the same scene can be found in the rushes video. We propose a method for detecting and clustering takes of one scene shot from the same or very similar camera positions. It uses a variant of the LCSS algorithm to find matching subsequences in sequences of visual features extracted from the source video. Hierarchical clustering is used to group the takes of one scene. The approach is evaluated in terms of correctly assigned takes using manually annotated ground truth.