Skip to Main Content
Texture classification of an image or subimage is an important problem in texture analysis. Many procedures have been proposed. A global framework for texture classification based on random closed set theory is proposed in this paper. In this approach, a binary texture is considered as an outcome of a random closed set. Some distributional descriptors of this stochastic model are used as texture features in order to classify the binary texture, in particular spherical and linear contact distributions and K-functions. If a grayscale texture has to be classified, then the original texture is reduced to a multivariate random closed set where each component (a different random set) corresponds with those pixels verifying a local property. Again, some functional descriptors of the multivariate random closed set defined from the texture can be used as texture features to describe and classify the grayscale texture. Marginal and cross spherical and linear contact distributions and K-functions have been used. Experimental validation is provided by using Brodatz's database and another standard texture database.