Automatic filter design for texture discrimination
Multichannel filtering has been shown by many researchers to provide good features for texture segmentation and classification. In this paper the authors exploit neural networks to construct optimal filters and to combine the outputs of these filters for the classification of known textures. The authors use the neural network training together with node pruning, so that both the classification error and the number of filters or, equivalently, the number of features, are minimized. The performance of the neural network classifier is demonstrated an several experiments involving classification of natural textures. The authors study the effects of using different sized filters with different network configurations. The authors show that the number of filters, and, therefore, the processing time, can be greatly reduced while preserving the classification accuracy, using the proposed scheme compared to using a general set of filters (e.g., Gabor filters).