The segmentation of video streams into their component shots is a pre-requisite for most applications involving content-based access to video libraries. In this paper, we address the segmentation problem from a probabilistic standpoint which exposes the major limitations of current solutions, and we suggest better alternatives derived from Bayesian principles. These principles lead to a framework which, by allowing the incorporation of prior knowledge about the video structure in its statistical model, leads to higher segmentation accuracy and provides a basis for content characterization which can later be used to categorize the video, retrieve it according to its content, or compare it to other instances stored in a database


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    Titel :

    A Bayesian video modeling framework for shot segmentation and content characterization


    Beteiligte:
    Vasconcelos, N. (Autor:in) / Lippman, A. (Autor:in)


    Erscheinungsdatum :

    01.01.1997


    Format / Umfang :

    905471 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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