We describe a self-organizing framework for content-based retrieval of images from large image databases at the object recognition level. The system uses the theories of optimal projection for optimal feature selection and a hierarchical image database for rapid retrieval rates. We demonstrate the query technique on a large database of widely varying real-world objects in natural settings, and show the applicability of the approach even for large variability within a particular object class.


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    Titel :

    Efficient content-based image retrieval using automatic feature selection


    Beteiligte:
    Swets, D.L. (Autor:in) / Weng, J.J. (Autor:in)


    Erscheinungsdatum :

    01.01.1995


    Format / Umfang :

    633095 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Efficient Content-Based Image Retrieval Using Automatic Feature Selection

    Swets, D. L. / Weng, J. J. / IEEE; Computer Society; Technical Committee for Pattern Analysis and Machine Intelligence | British Library Conference Proceedings | 1995



    Probabilistic Feature Relevance Learning for Content-Based Image Retrieval

    Peng, J. / Bhanu, B. / Qing, S. | British Library Online Contents | 1999