In this paper, we have proposed a novel framework to achieve more effective classifier training by using unlabeled samples. By integrating concept hierarchy for semantic image concept organization, a hierarchical mixture model is proposed to enable multi-level image concept modeling and hierarchical classifier training. To effectively learn the base-level classifiers for the atomic image concepts at the first level of the concept hierarchy, we have proposed a novel adaptive EM algorithm to achieve more effective classifier training with higher prediction accuracy. To effectively learn the classifiers for the higher-level semantic image concepts, we have also proposed a novel technique for classifier combining by using hierarchical mixture model. The experimental results on two large-scale image databases are also provided.


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    Title :

    Learning the semantics of images by using unlabeled samples


    Contributors:
    Fan, J. (author) / Luo, H. (author) / Gao, Y. (author)


    Publication date :

    2005-01-01


    Size :

    449153 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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