Statistical Contourlet Subband Characterization for Texture Image Retrieval

Abstract

The contourlet transform was recently developed to overcome the limitations of the wavelet transform. In this paper, we propose an effective and efficient texture retrieval method based on a group of six statistics of the coefficients in each contourlet subband of the texture image. In particular, six vectors are constructed respectively by these six statistics from the same directional subbands at different scales as well as the low-pass subband to serve as the directional characteristics of the image. By investigating the distributions of these vectors, we employ a weighted L₁ distance between two vectors of statistics to define a new distance between two images by summing up all the distances between the two corresponding vectors, with which the texture retrieval can be implemented according to the least distance criterion. Experimental results reveal that our approach outperforms some current state-of-the-art texture retrieval approaches.