Towards Genetic Programming for Texture Classification

Authors:
Andy Song;Thomas Loveard;Victor Ciesielski
Affiliations:
-;-;-
Venue:
AI '01 Proceedings of the 14th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Year:
2001

Citing 7
Cited 7

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Abstract

The genetic programming (GP) method is proposed as a new approach to perform texture classification based directly on raw pixel data. Two alternative genetic programming representations are used to perform classification. These are dynamic range selection (DRS) and static range selection (SRS). This preliminary study uses four brodatz textures to investigate the applicability of the genetic programming method for binary texture classifications and multi-texture classifications.Results indicate that the genetic programming method, based directly on raw pixel data, is able to accurately classify different textures. The results show that the DRS method is well suited to the task of texture classification. The classifiers generated in our experiments by DRS have good performance over a variety of texture data and offer GP as a promising alternative approach for the difficult problem of texture classification.