An adaptive model for texture classification

This paper presents an adaptive texture model for texture classification. In this model, a texture is considered containing both structural and stochastic components. These two components are indeterministic and deterministic parts as in the Wold texture model that are represented by Gaussian Markov random field (GMRF) model and multichannel filtering model based on Gabor function (Gabor model), respectively. According to the different ratio of composition from each component in the texture model, an adaptive factor was proposed for the new adaptive model. Experiments demonstrated that the new adaptive model can better represent a wide variety of textures and hence can lead to better classification results