Concept learning in content-based image retrieval (CBIR) systems is a challenging task. We present an active concept learning approach based on mixture model to deal with the two basic aspects of a database system: changing (image insertion or removal) nature of a database and user queries. To achieve concept learning, we develop a novel model selection method based on Bayesian analysis that evaluates the consistency of hypothesized models with the available information. The analysis of exploitation vs. exploration in the search space helps to find optimal model efficiently. Experimental results on Corel database show the efficacy of our approach.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Active concept learning for image retrieval in dynamic databases


    Beteiligte:
    Dong, (Autor:in) / Bhanu, (Autor:in)


    Erscheinungsdatum :

    01.01.2003


    Format / Umfang :

    396197 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Active Concept Learning for Image Retrieval in Dynamic Databases

    Dong, A. / Bhanu, B. / IEEE | British Library Conference Proceedings | 2003


    Content-Based Image Retrieval from Large Medical Databases

    Kak, A. / Pavlopoulou, C. | British Library Conference Proceedings | 2002


    FIRST: Fractal Indexing and Retrieval SysTem for Image Databases

    Nappi, M. / Polese, G. / Tortora, G. | British Library Online Contents | 1998


    FIRST: Fractal Indexing and Retrieval SysTem for Image Databases

    Nappi, M. / Polese, G. / Tortora, G. | British Library Online Contents | 1998


    Stochastic exploration and active learning for image retrieval

    Cord, M. / Gosselin, P. H. / Philipp-Foliguet, S. | British Library Online Contents | 2007