Model distribution dependant complexity estimation on textures

Abstract

On this work a method for the complexity of a textured image to be estimated is presented. The method allow to detect changes on its stationarity by means of the complexity with respect to a given model set (distribution dependant). That detection is done in such a way that also allows to classify textured images according to the whole texture complexity. When different models are used to model data, the more complex model is expected to fit it better because of the higher degree of freedom. Thus, a naturally-arisen penalization on the model complexity is used in a Bayesian context. Here a nested models scheme is used to improve the robustness and efficiency on the implementation. Even when MRF models are used for the sake of clarity, the procedure it is not subject to a particular distribution.