An Inhomogeneous Bayesian Texture Model for Spatially Varying Parameter Estimation


Dharmagunawardhana, Chathurika, Mahmoodi, Sasan, Bennett, Michael and Niranjan, Mahesan (2014) An Inhomogeneous Bayesian Texture Model for Spatially Varying Parameter Estimation At 3rd International Conference on Pattern Recognition Applications and Methods, France. , pp. 139-146.

Download

[img] PDF ICPRAM_2014_15_CR.pdf - Other
Download (4MB)

Description/Abstract

In statistical model based texture feature extraction, features based on spatially varying parameters achieve
higher discriminative performances compared to spatially constant parameters. In this paper we formulate a
novel Bayesian framework which achieves texture characterization by spatially varying parameters based on
Gaussian Markov random fields. The parameter estimation is carried out by Metropolis-Hastings algorithm.
The distributions of estimated spatially varying parameters are then used as successful discriminant texture
features in classification and segmentation. Results show that novel features outperform traditional Gaussian
Markov random field texture features which use spatially constant parameters. These features capture both
pixel spatial dependencies and structural properties of a texture giving improved texture features for effective
texture classification and segmentation.

Item Type: Conference or Workshop Item (Paper)
Venue - Dates: 3rd International Conference on Pattern Recognition Applications and Methods, France, 2014-03-01
Organisations: Southampton Wireless Group
ePrint ID: 361682
Date :
Date Event
March 2014Published
Date Deposited: 29 Jan 2014 16:06
Last Modified: 17 Apr 2017 14:21
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/361682

Actions (login required)

View Item View Item