By Topic

Texture classification using a probabilistic neural network and constraint satisfaction model

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
P. P. Raghu ; Dept. of Comput. Sci. & Eng., Indian Inst. of Technol., Madras, India ; B. Yegnanarayana

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

Published in:

Neural Networks, 1996., IEEE International Conference on  (Volume:1 )

Date of Conference:

3-6 Jun 1996