Automatic image categorization using low-level features is a challenging research topic in computer vision. In this paper, we formulate the image categorization problem as a multiple-instance learning (MIL) problem by viewing an image as a bag of instances, each corresponding to a region obtained from image segmentation. We propose a new solution to the resulting MIL problem. Unlike many existing MIL approaches that rely on the diverse density framework, our approach performs an effective feature mapping through a chosen metric distance function. Thus the MIL problem becomes solvable by a regular classification algorithm. Sparse SVM is adopted to dramatically reduce the regions that are needed to classify images. The selected regions by a sparse SVM approximate to the target concepts in the traditional diverse density framework. The proposed approach is a lot more efficient in computation and less sensitive to the class label uncertainty. Experimental results are included to demonstrate the effectiveness and robustness of the proposed method.


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    Title :

    A sparse support vector machine approach to region-based image categorization


    Contributors:
    Jinbo Bi, (author) / Yixin Chen, (author) / Wang, J.Z. (author)


    Publication date :

    2005-01-01


    Size :

    214532 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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