Bayesian Texture Classification From Indirect Observations Using Fast Sampling
A Bayesian method for texture model choice from blurred and noisy (i.e., indirect) observations is presented. The textures are modeled by stationary Random Fields, with various distribution laws, either Gaussian or Scale Mixtures of Gaussians. The power spectral densities of the fields are modeled by parametric functions and the aim is to select the most appropriate model among a set of candidates. This is achieved by computing the a posteriori model probabilities through parameter marginalization. The marginalization is done by sampling and harmonic mean approach, considering separately each model, in a within-model sampling strategy. The highly nonlinear dependency with respect to the parameters imposes the use of the Metropolis-Hastings sampler. Moreover, to achieve efficient sampling, the paper proposes a new fast algorithm based on the Fisher information matrix, the Fisher Metropolis-Hastings.