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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    Title :

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


    Contributors:
    Heisele, B. (author) / Serre, T. (author) / Mukherjee, S. (author) / Poggio, T. (author)


    Publication date :

    2001-01-01


    Size :

    879132 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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