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An Inhomogeneous Bayesian Texture Model for Spatially Varying Parameter Estimation

Record type: Conference or Workshop Item (Paper)

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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Citation

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.

More information

Published date: March 2014
Venue - Dates: 3rd International Conference on Pattern Recognition Applications and Methods, 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

Catalogue record

Date deposited: 29 Jan 2014 16:06
Last modified: 18 Jul 2017 02:59

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Contributors

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

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