Probabilistic image retrieval approaches can lead to significant gains over standard retrieval techniques. However, this occurs at the cost of a significant increase in computational complexity. In fact, closed-form solutions for probabilistic retrieval are currently available only for simple representations such as the Gaussian and the histogram. We analyze the case of mixture densities and exploit the asymptotic equivalence between likelihood and Kullback-Leibler divergence to derive solutions for these models. In particular, (1) we show that the divergence can be computed exactly for vector quantizers and, (2) has an approximate solution for Gaussian mixtures that introduces no significant degradation of the resulting similarity judgments. In both cases, the new solutions have closed-form and computational complexity equivalent to that of standard retrieval approaches, but significantly better retrieval performance.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    On the complexity of probabilistic image retrieval


    Beteiligte:


    Erscheinungsdatum :

    01.01.2001


    Format / Umfang :

    767363 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    On the Complexity of Probabilistic Image Retrieval

    Vasconcelos, N. / IEEE | British Library Conference Proceedings | 2001


    Medical image retrieval based on complexity analysis

    Backes, A. R. / Bruno, O. M. | British Library Online Contents | 2010


    Probabilistic Feature Relevance Learning for Content-Based Image Retrieval

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


    A probabilistic semantic model for image annotation and multimodal image retrieval

    Ruofei Zhang, / Zhongfei Zhang, / Mingjing Li, et al. | IEEE | 2005


    A Probabilistic Semantic Model for Image Annotation and Multi-Modal Image Retrieval

    Zhang, R. / Zhang, Z. / Li, M. et al. | British Library Conference Proceedings | 2005