The University of Southampton
University of Southampton Institutional Repository

Unsupervised Texture Segmentation using Active Contours and Local Distributions of Gaussian Markov Random Field Parameters

Unsupervised Texture Segmentation using Active Contours and Local Distributions of Gaussian Markov Random Field Parameters
Unsupervised Texture Segmentation using Active Contours and Local Distributions of Gaussian Markov Random Field Parameters
In this paper, local distributions of low order Gaussian Markov Random Field (GMRF) model parameters are proposed as texture features for unsupervised texture segmentation.
Instead of using model parameters as texture features, we exploit the variations in parameter estimates found by model fitting in local region around the given pixel. The
spatially localized estimation process is carried out by maximum likelihood method employing a moderately small estimation window which leads to modeling of partial texture
characteristics belonging to the local region. Hence significant fluctuations occur in the estimates which can be related to texture pattern complexity. The variations occurred in estimates are quantified by normalized local histograms. Selection of an accurate window size for histogram calculation is crucial and is achieved by a technique based on the entropy of textures. These texture features expand the possibility of using relatively
low order GMRF model parameters for segmenting fine to very large texture patterns and offer lower computational cost. Small estimation windows result in better boundary
localization. Unsupervised segmentation is performed by integrated active contours, combining the region and boundary information. Experimental results on statistical and structural component textures show improved discriminative ability of the features compared to some recent algorithms in the literature.
Dharmagunawardhana, Chathurika
68c6fe3a-ccce-4580-91ee-ec73fc2f9d07
Mahmoodi, Sasan
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf
Bennett, Michael
6df5585a-3d93-4870-8797-389759fc82c7
Mahesan, Niranjan
7967fd05-9b4f-41aa-be15-09cfca9b02d0
Dharmagunawardhana, Chathurika
68c6fe3a-ccce-4580-91ee-ec73fc2f9d07
Mahmoodi, Sasan
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf
Bennett, Michael
6df5585a-3d93-4870-8797-389759fc82c7
Mahesan, Niranjan
7967fd05-9b4f-41aa-be15-09cfca9b02d0

Dharmagunawardhana, Chathurika, Mahmoodi, Sasan, Bennett, Michael and Mahesan, Niranjan (2012) Unsupervised Texture Segmentation using Active Contours and Local Distributions of Gaussian Markov Random Field Parameters. 23rd British Machine Vision Conference, United Kingdom. 11 pp .

Record type: Conference or Workshop Item (Poster)

Abstract

In this paper, local distributions of low order Gaussian Markov Random Field (GMRF) model parameters are proposed as texture features for unsupervised texture segmentation.
Instead of using model parameters as texture features, we exploit the variations in parameter estimates found by model fitting in local region around the given pixel. The
spatially localized estimation process is carried out by maximum likelihood method employing a moderately small estimation window which leads to modeling of partial texture
characteristics belonging to the local region. Hence significant fluctuations occur in the estimates which can be related to texture pattern complexity. The variations occurred in estimates are quantified by normalized local histograms. Selection of an accurate window size for histogram calculation is crucial and is achieved by a technique based on the entropy of textures. These texture features expand the possibility of using relatively
low order GMRF model parameters for segmenting fine to very large texture patterns and offer lower computational cost. Small estimation windows result in better boundary
localization. Unsupervised segmentation is performed by integrated active contours, combining the region and boundary information. Experimental results on statistical and structural component textures show improved discriminative ability of the features compared to some recent algorithms in the literature.

Text
BMVC2012.pdf - Other
Download (3MB)

More information

Published date: 3 September 2012
Venue - Dates: 23rd British Machine Vision Conference, United Kingdom, 2012-09-03
Organisations: Centre of Excellence for International Banking, Finance & Accounting

Identifiers

Local EPrints ID: 342291
URI: http://eprints.soton.ac.uk/id/eprint/342291
PURE UUID: 6de4ea59-497f-472f-89bb-9fb5caaf0dc1

Catalogue record

Date deposited: 20 Aug 2012 11:13
Last modified: 19 Jul 2019 21:54

Export record

Contributors

Author: Chathurika Dharmagunawardhana
Author: Sasan Mahmoodi
Author: Michael Bennett
Author: Niranjan Mahesan

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×