Pp. Raghu et B. Yegnanarayana, SUPERVISED TEXTURE CLASSIFICATION USING A PROBABILISTIC NEURAL-NETWORK AND CONSTRAINT SATISFACTION MODEL, IEEE transactions on neural networks, 9(3), 1998, pp. 516-522
In this paper, the texture classification problem is projected as a co
nstraint satisfaction problem. The focus is on the use of a probabilis
tic neural network (PNN) for representing the distribution of feature
vectors of each texture class in order to generate a feature-label int
eraction constraint. This distribution of features for each class is a
ssumed as a Gaussian mixture model. The feature-label interactions and
a set of label-label interactions are represented on a constraint sat
isfaction neural network. A stochastic relaxation strategy is used to
obtain an optimal classification of textures in an image. The advantag
e of this approach is that all classes in an image are determined simu
ltaneously, similar to human perception of textures in an image.