This paper proposes a method to automatically set indexes which describe the content of a baseball video scene. The approach is based on the technique to patternize baseball scenes using a set of rectangles with some image features and a motion vector. Each baseball scene is expressed as a sequence of symbols based on the patternized data of every shot and given "event-indexes" (home-run, single-hit, walk, etc.) using hidden Markov models which have been trained to the sequences. The processes to construct training data and to set event-indexes to baseball scenes are described in detail. The proposed method is evaluated by an experiment using seven major league baseball video games.


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    Title :

    Baseball video indexing using patternization of scenes and hidden Markov model


    Contributors:
    Mochizuki, T. (author) / Tadenuma, M. (author) / Yagi, N. (author)


    Publication date :

    2005-01-01


    Size :

    437913 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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