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Color texture retrieval through contourlet-based hidden Markov model

Two statistical models for color texture retrieval based on a hidden Markov model (HMM) in the contourlet domain are described in this paper. Through a contourlet transformation, each color component of an image is decomposed into a set of directional subbands with texture details captured in different orientations. By exploiting inter-scale dependencies and in-band spatial dependencies, the distribution of the coefficients in each subband triplet (subbands of three color components at the same scale with the same orientation) can be estimated using a vector hidden Markov model. The Kullback-Leibler distance (KLD) is used to measure the difference between the distributions of query texture images and those of images in the database. The experimental results show the proposed retrieval systems yield high retrieval rates and better visual quality as compared with previous methods employing hidden Markov models for luminance component alone.