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Hierarchical Modeling and Adaptive Clustering for Real-time Summarization of Rush Videos

Main information
In this paper, we provide detailed descriptions of a proposed new algorithm for video summarization, which are also included in our submission to TRECVID’08 on BBC rush summarization. Firstly, rush videos are hierarchically modeled using the formal language technique. Secondly, shot detection are applied to introduce a new concept of V-unit for structuring videos in line with the hierarchical model, and thus junk frames within the model are effectively removed. Thirdly, adaptive clustering is employed to group shots into clusters to determine retakes for redundancy removal. Finally, each most representative shot selected from every cluster is ranked according to its length and sum of activity level for summarization. Competitive results have been achieved to prove the effectiveness and efficiency of our techniques, which are fully implemented in the compressed domain. Our work does not require high-level semantics such as object detection and speech/audio analysis which provides a more flexible and general solution for this topic. 
No referenced Knowhow defined
WP5: Detection, Extraction and Annotation of Knowledge. 
School of Informatics University of Bradford  Jianmin Jiang, Jinchang Ren 
2009-04-16 10:59  Request for more detail


Access and Use Rights
For LIVE project internal research purposes only.  For LIVE project internal development purposes only. 


Protection Status
Copyright School of Informatics, University of Bradford.  Patent Protection not sought. 
Licensing Protection not sought.  Utility Model Protection not sought. 
 
To appear in IEEE Trans. Multimedia 
Closed, attachment is not public 


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