As image collections become ever larger, effective access to their content requires a meaningful categorization of the images. Such a categorization can rely on clustering methods working on image features, but should greatly benefit from any form of supervision the user can provide, related to the visual content. Semi-supervised clustering - learning from both labelled and unlabelled data - has consequently become a topic of significant interest. In this paper we present a new semi-supervised clustering algorithm, pairwise-constrained competitive agglomeration, which is based on a fuzzy cost function that takes pairwise constraints into account.


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    Title :

    Semi-supervised image database categorization using pairwise constraints


    Contributors:
    Grira, N. (author) / Crucianu, M. (author) / Boujemaa, N. (author)


    Publication date :

    2005-01-01


    Size :

    196771 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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