Genetic Algorithms for Automated Texture Classification
With Jennifer Davidson
A shorter version appeared in:
Proceedings of SPIE, Vol. 3167

Abstract PDF eprint

In this paper we demonstrate that a genetic algorithm can be used to produce collections of pixel locations termed foot patterns useful for distinguishing between different types of texture images. The genetic algorithm minimizes the entropy of empirical samples taken with a particular foot pattern on a training image. The resulting low entropy foot patterns for several texture types are then used to classify test images. In order to classify a given image, foot patterns for several texture types are applied to the image to obtain entropy scores. The lowest entropy foot patterns are then used in a vote with the majority among the ten lowest scoring being taken as the classification. On the original test set of sixty images, twelve each from five image types, the resulting classification was 98.3% accurate (one image was not classified). When a sixth texture type, picked specifically to confound the classification technique, was added to texture types in the original test the technique misclassified several images of the two similar types. This latter experiment helps explain much of the how and why of the texture classification technique. We discuss potential methods for overcoming limitations of the texture classification technique.