Texture Modelling with Nested High-order Markov-Gibbs Random Fields

TL;DR

This paper introduces a novel approach to texture modelling using nested high-order Markov-Gibbs Random Fields, incorporating local binary patterns and a new learning framework for improved texture synthesis and analysis.

Contribution

It presents an explicit high-order MGRF structure with efficient feature learning, integrating local binary patterns with learned offsets, and introduces a texture-specific maximum likelihood objective for better generalization.

Findings

Successful texture synthesis across diverse textures

Competitive performance in texture grading and inpainting tasks

Effective high-order feature selection methods

Abstract

Currently, Markov-Gibbs random field (MGRF) image models which include high-order interactions are almost always built by modelling responses of a stack of local linear filters. Actual interaction structure is specified implicitly by the filter coefficients. In contrast, we learn an explicit high-order MGRF structure by considering the learning process in terms of general exponential family distributions nested over base models, so that potentials added later can build on previous ones. We relatively rapidly add new features by skipping over the costly optimisation of parameters. We introduce the use of local binary patterns as features in MGRF texture models, and generalise them by learning offsets to the surrounding pixels. These prove effective as high-order features, and are fast to compute. Several schemes for selecting high-order features by composition or search of a small subclass are compared. Additionally we present a simple modification of the maximum likelihood as a texture modelling-specific objective function which aims to improve generalisation by local windowing of statistics. The proposed method was experimentally evaluated by learning high-order MGRF models for a broad selection of complex textures and then performing texture synthesis, and succeeded on much of the continuum from stochastic through irregularly structured to near-regular textures. Learning interaction structure is very beneficial for textures with large-scale structure, although those with complex irregular structure still provide difficulties. The texture models were also quantitatively evaluated on two tasks and found to be competitive with other works: grading of synthesised textures by a panel of observers; and comparison against several recent MGRF models by evaluation on a constrained inpainting task.