We present a two-step method to speed-up object detection systems in computer vision that use Support Vector Machines (SVMs) as classifiers. In a first step we perform feature reduction by choosing relevant image features according to a measure derived from statistical learning theory. In a second step we build a hierarchy of classifiers. On the bottom level, a simple and fast classifier analyzes the whole image and rejects large parts of the background On the top level, a slower but more accurate classifier performs the final detection. Experiments with a face detection system show that combining feature reduction with hierarchical classification leads to a speed-up by a factor of 170 with similar classification performance.


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    Titel :

    Feature reduction and hierarchy of classifiers for fast object detection in video images


    Beteiligte:
    Heisele, B. (Autor:in) / Serre, T. (Autor:in) / Mukherjee, S. (Autor:in) / Poggio, T. (Autor:in)


    Erscheinungsdatum :

    01.01.2001


    Format / Umfang :

    879132 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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