Comparison of ML parameter estimation and neural network classifier for texture classification

Reena Singh; Ramon E. Vasquez; Rajeev Singh

doi:10.1117/12.269767

1 April 1997 Comparison of ML parameter estimation and neural network classifier for texture classification

Reena Singh, Ramon E. Vasquez, Rajeev Singh

Proceedings Volume 3030, Applications of Artificial Neural Networks in Image Processing II; (1997) https://doi.org/10.1117/12.269767
Event: Electronic Imaging '97, 1997, San Jose, CA, United States

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.

Citation Download Citation

Reena Singh, Ramon E. Vasquez, and Rajeev Singh "Comparison of ML parameter estimation and neural network classifier for texture classification", Proc. SPIE 3030, Applications of Artificial Neural Networks in Image Processing II, (1 April 1997); https://doi.org/10.1117/12.269767

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