Texture Similarity Measure Using Kullback-Leibler Divergence between Gamma Distributions

We propose a texture similarity measure based on the Kullback-Leibler divergence between gamma distributions (KLGamma). We conjecture that the spatially smoothed Gabor filter magnitude responses of some classes of visually homogeneous stochastic textures are gamma distributed. Classification experiments with disjoint test and training images, show that the KLGamma measure performs better than other parametric measures. It approaches, and under some conditions exceeds, the classification performance of the best non-parametric measures based on binned marginal histograms, although it has a computational cost at least an order of magnitude less. Thus, the KLGamma measure is well suited for use in real-time image segmentation algorithms and time-critical texture classification and retrieval from large databases.