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Proceedings Paper

Comparison of ML parameter estimation and neural network classifier for texture classification
Author(s): Reena Singh; Ramon E. Vasquez; Rajeev Singh
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Paper Abstract

The ML parameter estimation and the neural network based methods for classifying the textures are compared in this paper. The comparison is based on the correct classification percentage. Certain constraints have been imposed on the classifiers which are using the same sample size, same number of features and same number of training and test feature vectors for both the classifiers. The classifiers use the energy of the dominant channels of a tree-structured wavelet transform as features. Experiments are performed with textures from the Brodatz album. All the textured images are of size 256 by 256 pixels with 256 gray levels. Selection of best feature set has been arrived at using the 'leave one out' approach. The results indicate that both the classifiers give comparable performance. However, the governing factors for their choice are the number of training samples, number of features, and the computational complexity for both the classifiers, and the size of the network, in specific, for the neural network.

Paper Details

Date Published: 1 April 1997
PDF: 10 pages
Proc. SPIE 3030, Applications of Artificial Neural Networks in Image Processing II, (1 April 1997); doi: 10.1117/12.269767
Show Author Affiliations
Reena Singh, Univ. of Puerto Rico/Mayaguez (United States)
Ramon E. Vasquez, Univ. of Puerto Rico/Mayaguez (United States)
Rajeev Singh, Univ. of Puerto Rico/Mayaguez (United States)


Published in SPIE Proceedings Vol. 3030:
Applications of Artificial Neural Networks in Image Processing II
Nasser M. Nasrabadi; Aggelos K. Katsaggelos, Editor(s)

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