Fast unsupervised Bayesian image segmentation with adaptive spatial regularisation

TL;DR

This paper introduces a fast, unsupervised Bayesian image segmentation method that automatically adapts spatial regularisation parameters during inference, combining total-variation denoising and clustering for efficient high-dimensional processing.

Contribution

It develops a novel Bayesian estimation approach that removes the regularisation parameter via marginalisation and uses SVA analysis for efficient, adaptive segmentation.

Findings

Achieves extremely fast convergence in image segmentation.

Produces accurate results comparable to state-of-the-art methods.

Automatically adjusts regularisation strength based on observed data.

Abstract

This paper presents a new Bayesian estimation technique for hidden Potts-Markov random fields with unknown regularisation parameters, with application to fast unsupervised K-class image segmentation. The technique is derived by first removing the regularisation parameter from the Bayesian model by marginalisation, followed by a small-variance-asymptotic (SVA) analysis in which the spatial regularisation and the integer-constrained terms of the Potts model are decoupled. The evaluation of this SVA Bayesian estimator is then relaxed into a problem that can be computed efficiently by iteratively solving a convex total-variation denoising problem and a least-squares clustering (K-means) problem, both of which can be solved straightforwardly, even in high-dimensions, and with parallel computing techniques. This leads to a fast fully unsupervised Bayesian image segmentation methodology in which the strength of the spatial regularisation is adapted automatically to the observed image during the inference procedure, and that can be easily applied in large 2D and 3D scenarios or in applications requiring low computing times. Experimental results on real images, as well as extensive comparisons with state-of-the-art algorithms, confirm that the proposed methodology offer extremely fast convergence and produces accurate segmentation results, with the important additional advantage of self-adjusting regularisation parameters.