Automated techniques to optimize feature descriptor weights and select optimum feature descriptor subset are desirable as a way to enhance the performance of content based image retrieval system. In our system, all the MPEG-7 image feature descriptors including color descriptors, texture descriptors and shape descriptors are used to represent low-level image features. We use a real coded chromosome genetic algorithm (GA) and k-nearest neighbor (k-NN) classification accuracy as fitness function to optimize weights. Meanwhile, a binary one and k-NN classification accuracy combining with the size of feature descriptor subset as fitness function are used to select optimum feature descriptor subset. Furthermore, we propose two kinds of two-stage feature selection schemes for weight optimization and descriptor subset selection, which are the integration of a real coded GA and a binary one. The experimental results over 2000 classified Corel images show that with weight optimization, the accuracy of image retrieval system is improved; with the selection of optimum feature descriptor subset, both the accuracy and the efficiency are improved.


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    Titel :

    Feature Selection Based on Genetic Algorithm for CBIR


    Beteiligte:
    Zhao, Tianzhong (Autor:in) / Lu, Jianjiang (Autor:in) / Zhang, Yafei (Autor:in) / Xiao, Qi (Autor:in)


    Erscheinungsdatum :

    2008-05-01


    Format / Umfang :

    409650 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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