An approach is proposed for clustering time-series data. The approach can be used to discover groupings of similar object motions that were observed in a video collection. A finite mixture of hidden Markov models (HMMs) is fitted to the motion data using the expectation maximization (EM) framework. Previous approaches for HMM-based clustering employ a k-means formulation, where each sequence is assigned to only a single HMM. In contrast, the formulation presented in this paper allows each sequence to belong to more than a single HMM with some probability, and the hard decision about the sequence class membership can be deferred until a later time when such a decision is required. Experiments with simulated data demonstrate the benefit of using this EM-based approach when there is more "overlap" in the processes generating the data. Experiments with real data show the promising potential of HMM-based motion clustering in a number of applications.


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    Title :

    Discovering clusters in motion time-series data


    Contributors:
    Alon, J. (author) / Sclaroff, S. (author) / Kollios, G. (author) / Pavlovic, V. (author)


    Publication date :

    2003-01-01


    Size :

    371521 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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