Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Rushes video summarization by object and event understanding
Proceedings of the international workshop on TRECVID video summarization
NTU TRECVID-2007 fast rushes summarization system
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
Automatic score scene detection for baseball video
LKR'08 Proceedings of the 3rd international conference on Large-scale knowledge resources: construction and application
Moving-Object Detection, Association, and Selection in Home Videos
IEEE Transactions on Multimedia
The trecvid 2008 BBC rushes summarization evaluation
TVS '08 Proceedings of the 2nd ACM TRECVid Video Summarization Workshop
Multi-document video summarization
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Automatic evaluation of video summaries
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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This paper describes our video summarization system using a model selection technique to estimate the optimal number of scenes for a summary. It uses a minimum description length as a model selection criterion and carries out two-stage estimation. First, we estimate the number of scenes in each shot, and then we estimate the number of scenes in a whole video clip. We model a set of scenes with a Gaussian mixture model, where the mixture component is assumed to represent one scene. Our system was evaluated in the TRECVID 2008 rushes summarization task, where the test video set was unedited materials provided by the BBC. Our scores were about the same as the average of all the participants for the eight evaluation measures.