We suggest a spectral histogram, defined as the marginal distribution of filter responses, as a quantitative definition for a texton pattern. By matching spectral histograms, an arbitrary image can be transformed to an image with similar textons to the observed. We use the χ2-statistic to measure the difference between two spectral histograms, which leads to a texture discrimination model. The performance of the model well matches psychophysical results on a systematic set of texture discrimination data and it exhibits the nonlinearity and asymmetry phenomena in human texture discrimination. A quantitative comparison with the Malik-Perona model is given, and a number of issues regarding the model are discussed. © 2002 Elsevier Science Ltd. All rights reserved.
Liu, X., & Wang, D. L. (2002). A spectral histogram model for texton modeling and texture discrimination. Vision Research, 42(23), 2617–2634. https://doi.org/10.1016/S0042-6989(02)00297-3