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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    Title :

    Topology free hidden Markov models: application to background modeling


    Contributors:
    Stenger, B. (author) / Ramesh, V. (author) / Paragios, N. (author) / Coetzee, F. (author) / Buhmann, J.M. (author)


    Publication date :

    2001-01-01


    Size :

    805662 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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