A Subspace Approach to Texture Modelling by Using Gaussian Mixtures

Assuming local and shift-invariant texture properties we describe the statistical dependencies between pixels by a joint probability density of gray-levels within a suitably chosen observation window. We estimate the unknown multivariate density in the form of a Gaussian mixture of product components from data obtained by shifting the observation window. Obviously, the size of the window should be large to capture the low-frequency properties of textures but, on the other hand, the increasing dimension of the estimated mixture may become prohibitive. By considering a subspace approach based on a structural mixture model we can increase the size of the observation window while keeping the computational complexity in reasonable bounds