Texture classification using a probabilistic neural network and constraint satisfaction model

In this paper, the texture classification problem is projected as a constraint satisfaction problem. The focus is on the use of a probabilistic neural network for representing the distribution of feature vectors of each texture class in order to generate a feature-label interaction constraint. This distribution is assumed as a Gaussian mixture model. The feature-label interactions and a set of label-label interactions are represented on a constraint satisfaction neural network. A stochastic relaxation strategy is used to obtain an optimal classification of the textured image