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Interactive CBIR using RBF-based relevance feedback for WT/VQ coded images

Powerful interfaces provide great potential for retrieval systems to adapt to dynamic user needs and allow a more accurate modeling of image similarity from the users' point of view. We propose a novel method within the interactive framework. It allows the users to directly modify the system characteristics by specifying their desired image attributes in the form of training samples. More specifically, we have adopted a radial basis function (RBF) method for implementing an adaptive metric which progressively models the notion of image similarity through continual feedback from the users. The proposed approach has been integrated into an image retrieval system using images compressed by wavelet transform and vector quantization coders. Comparisons with some of the recent systems using the standard texture database indicate that the proposed method provides the more favorable retrieval result.