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Content description and representation are still challenging issues for the design and management of content-based image retrieval systems. This work proposes to derive content descriptors of color images by wavelet coding and indexing of the HSV (Hue, Saturation, Value) channels. An efficient scheme for this problem has to trade between being translation and rotation invariant, fast and accurate at the same time. Based on a diverse and difficult database of 1020 color images, and a strong experimental protocol we propose a method that first divides an image into 9 rectangular regions (i.e. zoning), second it applies a wavelet transformation in each of the HSV channels. A subset of the approximation and of detail coefficients of each set is then selected. A similarity measure based on histogram intersection followed by vector distance computation for the 9 regions then evaluates and ranks the closest images of the database by content. In this paper we give the details of the this new approach and show promising results upon extensive experiments performed in our lab.

Evaluating Content-Based Image Retrieval by Combining Color and Wavelet Features in a Region Based Scheme