A linear compositional model for analyzing and classifying image textures

This paper presents a texture analysis method using a newly developed linear compositional texture model. In this model, an image texture is considered to be a linear composition of both structural and random components. By using a Wold-like texture decomposition, an image texture can be decomposed into two orthogonal fields, namely, the deterministic field and the purely indeterministic field. By the concept of linear composition of the two components, we propose the composition ratio of the two components to be presented by the proportion of their spectral energies. The two components are also individually represented by using the multi-channel filtering model with Gabor wavelet and the Gaussian Markov random field model, respectively. The linear compositional texture model thus extends the representation and analysis capacity of the model to deal with a wider variety of image textures.