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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    Title :

    Automatic video summarization by graph modeling


    Contributors:


    Publication date :

    2003-01-01


    Size :

    301075 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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