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

Jinchang Ren, Jianmin Jiang

Research output: Contribution to journalArticlepeer-review

45 Citations (Scopus)
185 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)906-917
Number of pages12
JournalIEEE Transactions on Multimedia
Issue number5
Publication statusPublished - Aug 2009


  • multimedia
  • pattern clustering
  • object detection
  • adaptive clustering
  • hierarchical modelling


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