Estimation of an interaction structure in Gibbs image modelling

Empirical and analytical methods of selecting a characteristic structure of pairwise pixel interactions in Gibbs random field texture models are compared. Simple thresholding of partial interaction energies recovers a basic structure for modelling spatially homogeneous stochastic textures. Computationally intensive empirical sequential learning reduces the size of a basic structure and complements it by a fine structure describing characteristic minor details of regular textures. The empirical selection implicitly assumes that characteristic structures should include only statistically independent interactions. It can be approximated using analytical estimates of energies. Experiments show that a combined analytical-empirical sequential learning finds reduced basic and fine interaction structures much faster than the purely empirical one, but the sequential selection based on relative interaction energies may deteriorate basic structures of stochastic textures with strongly interdependent characteristic interactions