Complexity-based border detection for textured images

In this paper we address the problem of border detection for textured images by exploiting the concept of Bayesian complexity minimization as a generalization of the MDL paradigm in the Bayesian context. We aim at determining if a small window at a certain location corresponds to a single or several texture classes, which here are modeled as Gaussian Markov Random Fields (GMRF), thereby detecting the presence of borders. For doing this, a set of possible border configurations are tested by applying a Bayesian decision rule that includes two terms: a classical likelihood term related to the model fitting error, and a complexity penalizing term for the number and size of the window subdivisions in each configuration. The latter is derived from the Bayesian Information Criterion (BIC) for non-causal Markov models related to the MDL decision rule. Experiments on synthetic and real textured images segmentation support the approach with promising results.