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Proceedings Paper

Genetic algorithms for automated texture classification
Author(s): Dan Ashlock; Jennifer L. Davidson
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Paper Abstract

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 binary 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.

Paper Details

Date Published: 14 October 1997
PDF: 12 pages
Proc. SPIE 3167, Statistical and Stochastic Methods in Image Processing II, (14 October 1997); doi: 10.1117/12.279637
Show Author Affiliations
Dan Ashlock, Iowa State Univ. (United States)
Jennifer L. Davidson, Iowa State Univ. (United States)

Published in SPIE Proceedings Vol. 3167:
Statistical and Stochastic Methods in Image Processing II
Francoise J. Preteux; Jennifer L. Davidson; Edward R. Dougherty, Editor(s)

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