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. 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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