<title>Unsupervised segmentation of images</title>

Abstract

We present an unsupervised segmentation algorithm comprising a simulated annealing process on a single Markov Chain to directly calculate the MAP segmentation over a viable number of regions. The algorithm is applied to both Isotropic and Gaussian Hierarchical Markov Random Field (MRF) Models, which may be combined with low level line processes. The annealing algorithm utilizes a sampling framework that unified the processes of model selection, parameter estimation and image segmentation in a single Markov Chain. To achieve this, reversible jumps are incorporated to allow movement between different model spaces. A new method for generating jump proposals is given, which is more efficient than existing methodologies and is applicable to other, less specific model selection problems. It is based on the use of partial decoupling, rather than the more traditional Gibbs Sampler, to update the labels of the MRF. Partial decoupling is a derivative of the better known Swendsen-Wang algorithm in which an auxiliary variable bondmap is used to define regions of the image whose labels are then updated independently. We further propose a novel mechanisms by which deterministic methods, such as Gabor filtering, may be incorporated into this algorithm to sped up the convergence of the MCMC sampling process and hence, that of simulated annealing.