Learning High-order Generative Texture Models

Abstract

We introduce a new simple framework for texture modelling with Markov--Gibbs random fields (MGRF). The framework learns texture-specific high order pixel interactions described by feature functions of signal patterns. Currently, modelling of high order interactions is almost exclusively achieved by linear filtering. Instead we investigate `binary pattern' (BP) features which are faster to compute and describe quite different properties than linear filters. The features are similar to local binary patterns (LBPs) --- previously not applied as MGRF features --- but with learnt shapes. In contrast to the majority of MGRF models the set of features used is learnt from the training data and is heterogeneous. This paper shows how these features can be efficiently selected by nesting the models. Each new layer corrects errors of the previous model while allowing incremental composition of the features, and uses validation data to decide the stopping point. The framework also reduces overfitting and speeds learning due to a feasible number of free parameters to be learnt at each step. Texture synthesis results of the proposed texture models were quantitatively evaluated by a panel of observers, showing higher order BP features resulted in significant improvements on regularly and irregularly structured textures.