A spatial correlation based method for neighbor set selection in random field image models

Random field (RF) models have widespread application in image modeling and analysis. The effectiveness of these models is largely dependent on the choice of neighbor sets, which determine the spatial interactions that are representable by the model. In this work, we consider the problem of selecting these neighbor sets for simultaneous autoregressive and Gauss–Markov random field models, based on the correlation structure of the image to be modeled. A procedure for identifying appropriate neighbor sets is proposed, and experimental results which demonstrate the viability of this method are presented.