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An Inhomogeneous Bayesian Texture Model for Spatially Varying Parameter Estimation

An Inhomogeneous Bayesian Texture Model for Spatially Varying Parameter Estimation
An Inhomogeneous Bayesian Texture Model for Spatially Varying Parameter Estimation
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.
139-146
Dharmagunawardhana, Chathurika
68c6fe3a-ccce-4580-91ee-ec73fc2f9d07
Mahmoodi, Sasan
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf
Bennett, Michael
6df5585a-3d93-4870-8797-389759fc82c7
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Dharmagunawardhana, Chathurika
68c6fe3a-ccce-4580-91ee-ec73fc2f9d07
Mahmoodi, Sasan
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf
Bennett, Michael
6df5585a-3d93-4870-8797-389759fc82c7
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f

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

Record type: Conference or Workshop Item (Paper)

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.

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More information

Published date: March 2014
Venue - Dates: 3rd International Conference on Pattern Recognition Applications and Methods, Angers, France, 2014-03-01
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 361682
URI: http://eprints.soton.ac.uk/id/eprint/361682
PURE UUID: 92efd87b-59e1-4a70-8260-8413072901d4
ORCID for Mahesan Niranjan: ORCID iD orcid.org/0000-0001-7021-140X

Catalogue record

Date deposited: 29 Jan 2014 16:06
Last modified: 15 Mar 2024 03:29

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Contributors

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

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