title: A spectral histogram model for texton modeling and texture discrimination.
abstract: 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 chi(2)-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.
authors.affiliation: Department of Computer Science, Florida State University, Tallahassee, FL 32306-4530, USA. liux@cs.fsu.edu
authors.forename: Xiuwen
authors.initials: X
authors.lastname: Liu
authors.forename: DeLiang
authors.initials: D
authors.lastname: Wang
mesh_terms: D004192:Discrimination, Psychological / Q000502:physiology*; D006801:Humans; D008954:Models, Biological; D010364:Pattern Recognition, Visual / Q000502:physiology*