Texture classification using a probabilistic neural network and constraint satisfaction model

Author

Raghu, P.P. ; Yegnanarayana, B.

Author_Institution

Dept. of Comput. Sci. & Eng., Indian Inst. of Technol., Madras, India

Volume

1

fYear

1996

fDate

3-6 Jun 1996

Firstpage

424

Abstract

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

Keywords

image classification; image texture; neural nets; Gaussian mixture model; constraint satisfaction model; feature-label interaction constraint; label-label interactions; optimal classification; probabilistic neural network; stochastic relaxation strategy; texture classification; textured image; Computer science; Electronic mail; Image classification; Integrated circuit modeling; Neural networks; Pixel; Random processes; Random variables; Statistical distributions; Stochastic processes;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1996., IEEE International Conference on

Conference_Location

Washington, DC

Print_ISBN

0-7803-3210-5

Type

conf

DOI

10.1109/ICNN.1996.548930

Filename

548930