Unsupervised image segmentation

By using partial decoupling to segment the MRF it is possible to generate jump proposals efficiently while providing a mechanism for the use of deterministic methods, such as Gabor filtering, to speed up convergence.

Abstract

We present an unsupervised segmentation algorithm comprising an annealing process to select the maximum a posteriori (MAP) realization of a hierarchical Markov random field (MRF) model. The algorithm consists of a sampling framework which unifies the processes of model selection, parameter estimation and image segmentation, in a single Markov chain. To achieve this, reversible jumps are incorporated into the Markov chain to allow movement between model spaces. By using partial decoupling to segment the MRF it is possible to generate jump proposals efficiently while providing a mechanism for the use of deterministic methods, such as Gabor filtering, to speed up convergence.