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Supervised texture classification using a probabilistic neural network and constraint satisfaction model
Research Area: Uncategorized Year: 1998
Type of Publication: Article Keywords: constraint handling, image classification, image texture, neural nets, probabilityGaussian mixture model, constraint satisfaction model, feature-label interaction constraint, optimal classification, probabilistic neural network, supervised texture classif
Authors: P.P. Raghu, B. Yegnanarayana  
Journal: IEEE Trans. Neural Networks Volume: 9
Number: 3 Pages: 516-522
Month: May.
   
Abstract:
The texture classification problem is projected as a constraint satisfaction problem. The focus is on the use of a probabilistic neural network (PNN) for representing the distribution of feature vectors of each texture class in order to generate a feature-label interaction constraint. This distribution of features for each class 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 textures in an image. The advantage of this approach is that all classes in an image are determined simultaneously, similar to human perception of textures in an image
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