An Efficient Histogram-Based Texture Classification Method with Weighted Symmetrized Kullback-Leibler Divergence

Abstract

In information processing using Wavelet transform, wavelet subband coefficients are often modelled by a probability distribution function. Recently, a local energy histogram method has been proposed to alleviate the difficulty in modeling wavelet subband coefficients with a previously assumed distribution function. Actually, the similarity between any two local energy histograms was measured by a symmetrized Kullback-Leibler divergence (SKLD). However, this measurement neglects the balance of wavelet subbands’ roles in texture classification. In this paper, we propose an efficient texture classification method based on weighted symmetrized Kullback-Leibler divergences (WSKLDs) between two local energy histograms (LEHs). In particular, for any test and training images, we index their Wavelet subbands in the same way, and weight the SKLD between any two LEHs of the s-th wavelet subbands of two image by the reciprocal of the summation of the SKLDs between the expected LEHs of any two different texture classes over all training images. Experimental results reveal that our proposed method outperforms five state-of-the-art methods.