Feature extraction for texture discrimination via random field models with random spatial interaction.

In this paper, we attack the problem of distinguishing textured images of real surfaces using small samples. We first analyze experimental data that results from applying ordinary conditional Markov fields. In the face of the disappointing performance of these models, we introduce a random field with spatial interaction that is itself a random variable (usually referred to as a random field in a random environment). For this class of models, we establish the power spectrum and the autocorrelation function as well-defined quantities, and we devise a scheme for the estimation of related parameters. The new set of features that resulted from this approach was applied to real images. Accurate discrimination was observed even for boxes of size 10x16.