Hidden Markov models (HMMs) are increasingly being used in computer vision for applications such as: gesture analysis, action recognition from video, and illumination modeling. Their use involves an off-line learning step that is used as a basis for on-line decision making (i.e. a stationarity assumption on the model parameters). But, real-world applications are often non-stationary in nature. This leads to the need for a dynamic mechanism to learn and update the model topology as well as its parameters. This paper presents a new framework for HMM topology and parameter estimation in an online, dynamic fashion. The topology and parameter estimation is posed as a model selection problem with an MDL prior. Online modifications to the topology are made possible by incorporating a state splitting criterion. To demonstrate the potential of the algorithm, the background modeling problem is considered. Theoretical validation and real experiments are presented.


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    Titel :

    Topology free hidden Markov models: application to background modeling


    Beteiligte:
    Stenger, B. (Autor:in) / Ramesh, V. (Autor:in) / Paragios, N. (Autor:in) / Coetzee, F. (Autor:in) / Buhmann, J.M. (Autor:in)


    Erscheinungsdatum :

    01.01.2001


    Format / Umfang :

    805662 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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