We propose a unified approach for summarization based on the analysis of video structures and video highlights. Our approach emphasizes both the content balance and perceptual quality of a summary. Normalized cut algorithm is employed to globally and optimally partition a video into clusters. A motion attention model based on human perception is employed to compute the perceptual quality of shots and clusters. The clusters, together with the computed attention values, form a temporal graph similar to Markov chain that inherently describes the evolution and perceptual importance of video clusters. In our application, the flow of a temporal graph is utilized to group similar clusters into scenes, while the attention values are used as guidelines to select appropriate subshots in scenes for summarization.


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    Titel :

    Automatic video summarization by graph modeling


    Beteiligte:
    Chong-Wah Ngo, (Autor:in) / Yu-Fei Ma, (Autor:in) / Hong-Jiang Zhang, (Autor:in)


    Erscheinungsdatum :

    01.01.2003


    Format / Umfang :

    301075 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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