Inference strategies for texture parameters

Abstract

The Autobinomial model is a commonplace in Bayesian image analysis since its introduction as a convenient image model. Such model depends on a set of parameters; their value characterize texture allowing to perform classification of the whole image into regions with uniform properties of the model. This work propose a new estimator of the parameter vector of the Autobinomial model based on Conditional Least Square minimization via Real Coded Genetic Modeling and analyze its performance compared to the classical linear approximation, which exchanges the CLS equation with a reduced Taylor equation prior to minimization. Our simulation study shows that the genetic modeling approach gives more accurate estimations when true data is provided. We also discuss its influence in a set of classification experiments with multispectral optical imagery, estimating the scalar vector parameter with our estimator and the classical linear one. Our experiments show promising results, since our approach is able to distinguish image features that the classical approach does not.