Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Video abstraction: A systematic review and classification
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Rushes video summarization by object and event understanding
Proceedings of the international workshop on TRECVID video summarization
The trecvid 2008 BBC rushes summarization evaluation
TVS '08 Proceedings of the 2nd ACM TRECVid Video Summarization Workshop
The trecvid 2008 BBC rushes summarization evaluation
TVS '08 Proceedings of the 2nd ACM TRECVid Video Summarization Workshop
Scene pathfinder: unsupervised clustering techniques for movie scenes extraction
Multimedia Tools and Applications
IM(S)2: Interactive movie summarization system
Journal of Visual Communication and Image Representation
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In this paper, we describe our system used to summarize BBC rushes, the TRECVID database. Our summarization process starts with shot boundary detection. Then we filter obtained shots to retain only useful ones. After that we try to localize from every retained shot the important parts (sub-shots). Finally, we select some of them to formulate the skim. The selection of sub-shots must respond to many criteria as redundancy removing, covering all important events of the original video sequence and not exceeding the upper duration. Genetic algorithms are naturally suited for doing incremental selection. We use it to do the selection of relevant subs-shots. We consider the summarization process as an optimization problem which takes into consideration all evoked criteria. The obtained results are encouraging.