Despite the efforts to reduce the semantic gap between user perception of similarity and feature-based representation of images, user interaction is essential to improve retrieval performance in content-based image retrieval. To this end a number of relevance feedback mechanisms are currently adopted to refine image queries. They are aimed either to locally modify the feature space or to shift the query point towards more promising regions of the feature space. A novel adaptive query shifting mechanism is proposed to improve retrieval performance beyond that provided by other relevance feedback mechanisms. In addition we discuss the extent to which query shifting may provide better performance than feature weighting and provide experimental results on the complementarity of the two approaches. Finally, some combinational approaches are proposed to exploit such complementarities.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Comparison and combination of adaptive query shifting and feature relevance learning for content-based image retrieval


    Contributors:
    Giacinto, G. (author) / Roli, F. (author) / Fumera, G. (author)


    Publication date :

    2001-01-01


    Size :

    547869 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Comparison and Combination of Adaptive Query Shifting and Feature Relevance Learning for Content-Based Image Retrieval

    Giacinto, G. / Roli, F. / Fumera, G. et al. | British Library Conference Proceedings | 2001


    Probabilistic Feature Relevance Learning for Content-Based Image Retrieval

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


    Image Retrieval: Feature Primitives, Feature Representation, and Relevance Feedback

    Zhou, X. / Huang, T. / IEEE | British Library Conference Proceedings | 2000


    Multi-class relevance feedback content-based image retrieval

    Peng, J. | British Library Online Contents | 2003