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

Ren, Jinchang and Jiang, J. (2009) Hierarchical modelling and adaptive clustering for real-time summarization of rush videos. IEEE Transactions on Multimedia, 11 (5). pp. 906-917. ISSN 1520-9210

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Abstract

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
ID code: 29268
Keywords: multimedia, pattern clustering, object detection, adaptive clustering, hierarchical modelling, Electronic computers. Computer science
Subjects: Science > Mathematics > Electronic computers. Computer science
Department: Faculty of Engineering > Electronic and Electrical Engineering
Related URLs:
    Depositing user: Pure Administrator
    Date Deposited: 15 Mar 2011 15:24
    Last modified: 12 Mar 2012 11:23
    URI: http://strathprints.strath.ac.uk/id/eprint/29268

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