Fast unsupervised Bayesian image segmentation with adaptive spatial regularisation

02/05/2015
by   Marcelo Pereyra, et al.
0

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.

READ FULL TEXT

page 18

page 19

page 20

page 21

research
05/30/2016

Image segmentation based on the hybrid total variation model and the K-means clustering strategy

The performance of image segmentation highly relies on the original inpu...
research
05/09/2020

A Weighted Difference of Anisotropic and Isotropic Total Variation for Relaxed Mumford-Shah Color and Multiphase Image Segmentation

In a class of piecewise-constant image segmentation models, we incorpora...
research
05/13/2023

Image Segmentation via Probabilistic Graph Matching

This work presents an unsupervised and semi-automatic image segmentation...
research
11/26/2019

Maximum likelihood estimation of regularisation parameters in high-dimensional inverse problems: an empirical Bayesian approach

Many imaging problems require solving an inverse problem that is ill-con...
research
02/09/2016

Bayesian nonparametric image segmentation using a generalized Swendsen-Wang algorithm

Unsupervised image segmentation aims at clustering the set of pixels of ...
research
09/21/2022

A Fast Algorithm for Implementation of Some Minimum L2 Distance Estimators and Their Application to Image Segmentation

Minimum distance estimation methodology based on empirical distribution ...
research
03/05/2018

Fast Implementation of a Bayesian Unsupervised Segmentation Algorithm

In a recent paper, we have proposed an unsupervised algorithm for audio ...

Please sign up or login with your details

Forgot password? Click here to reset