We propose a novel approach to learn and recognize natural scene categories. Unlike previous work, it does not require experts to annotate the training set. We represent the image of a scene by a collection of local regions, denoted as codewords obtained by unsupervised learning. Each region is represented as part of a "theme". In previous work, such themes were learnt from hand-annotations of experts, while our method learns the theme distributions as well as the codewords distribution over the themes without supervision. We report satisfactory categorization performances on a large set of 13 categories of complex scenes.


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    Title :

    A Bayesian hierarchical model for learning natural scene categories


    Contributors:
    Fei-Fei, L. (author) / Perona, P. (author)


    Publication date :

    2005-01-01


    Size :

    2117095 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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