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.