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Hierarchical modeling and adaptive clustering for real-time summarization of rush videos

Ren, J and Jiang, J (2009) Hierarchical modeling and adaptive clustering for real-time summarization of rush videos IEEE Transactions on Multimedia, 11 (5). pp. 906-917.

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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 detections 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.

Item Type: Article
Divisions : Surrey research (other units)
Authors :
Ren, J
Date : 2009
DOI : 10.1109/TMM.2009.2021782
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 12:25
Last Modified : 24 Jan 2020 22:12

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