A general classification framework, called boosting chain, is proposed for learning boosting cascade. In this framework, a "chain" structure is introduced to integrate historical knowledge into successive boosting learning. Moreover, a linear optimization scheme is proposed to address the problems of redundancy in boosting learning and threshold adjusting in cascade coupling. By this means, the resulting classifier consists of fewer weak classifiers yet achieves lower error rates than boosting cascade in both training and test. Experimental comparisons of boosting chain and boosting cascade are provided through a face detection problem. The promising results clearly demonstrate the effectiveness made by boosting chain.


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    Title :

    Boosting chain learning for object detection


    Contributors:
    Rong Xiao, (author) / Long Zhu, (author) / Hong-Jiang Zhang, (author)


    Publication date :

    2003-01-01


    Size :

    593481 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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