Significant time and effort has been devoted to finding feature representations of images in databases in order to enable content-based image retrieval (CBIR). Relevance feedback is a mechanism for improving retrieval precision over time by allowing the user to implicitly communicate to the system which of these features are relevant and which are not. We propose a relevance feedback retrieval system that, for each retrieval iteration, learns a decision tree to uncover a common thread between all images marked as relevant. This tree is then used as a model for inferring which of the unseen images the user would not likely desire. We evaluate our approach within the domain of HRCT images of the lung.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Relevance feedback decision trees in content-based image retrieval


    Beteiligte:
    MacArthur, (Autor:in) / Brodley, (Autor:in) / Chi-Ren Shyu, (Autor:in)


    Erscheinungsdatum :

    2000-01-01


    Format / Umfang :

    105674 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Relevance Feedback Decision Trees in Content-based Image Retrieval

    MacArthur, S. / Brodley, C. / Shyu, C. et al. | British Library Conference Proceedings | 2000


    Multi-class relevance feedback content-based image retrieval

    Peng, J. | British Library Online Contents | 2003



    Interactive Content-Based Image Retrieval Using Relevance Feedback

    MacArthur, S. D. / Brodley, C. E. / Kak, A. C. et al. | British Library Online Contents | 2002


    Bayesian Relevance Feedback for Content-based Image Retrieval

    Vasconcelos, N. / Lippman, A. / IEEE | British Library Conference Proceedings | 2000