An Inhomogeneous Bayesian Texture Model for Spatially Varying Parameter Estimation

Chathurika Dharmagunawardhana, Sasan Mahmoodi, Michael Bennett, Mahesan Niranjan

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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Paper Citation


in Harvard Style

Dharmagunawardhana C., Mahmoodi S., Bennett M. and Niranjan M. (2014). An Inhomogeneous Bayesian Texture Model for Spatially Varying Parameter Estimation . In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-018-5, pages 139-146. DOI: 10.5220/0004752501390146


in Bibtex Style

@conference{icpram14,
author={Chathurika Dharmagunawardhana and Sasan Mahmoodi and Michael Bennett and Mahesan Niranjan},
title={An Inhomogeneous Bayesian Texture Model for Spatially Varying Parameter Estimation},
booktitle={Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2014},
pages={139-146},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004752501390146},
isbn={978-989-758-018-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - An Inhomogeneous Bayesian Texture Model for Spatially Varying Parameter Estimation
SN - 978-989-758-018-5
AU - Dharmagunawardhana C.
AU - Mahmoodi S.
AU - Bennett M.
AU - Niranjan M.
PY - 2014
SP - 139
EP - 146
DO - 10.5220/0004752501390146