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Towards Genetic Programming for Texture Classification

Towards Genetic Programming for Texture Classification

Book SeriesLecture Notes in Computer Science
PublisherSpringer Berlin / Heidelberg
ISSN0302-9743 (Print) 1611-3349 (Online)
VolumeVolume 2256/2001
BookAI 2001: Advances in Artificial Intelligence
DOI10.1007/3-540-45656-2
Copyright2001
ISBN978-3-540-42960-9
DOI10.1007/3-540-45656-2_40
Pages557-573
Subject CollectionComputer Science
SpringerLink DateMonday, January 01, 2001
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Towards Genetic Programming for Texture Classification

Andy Song3 Contact Information, Thomas Loveard3 Contact Information and Vic Ciesielski3 Contact Information

(3)  School of Computer Science, RMIT University, GPO Box 2476V, 3001 Melbourne, Victoria, Australia
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.

Contact Information Andy Song
Email: asong@cs.rmit.edu.au

Contact Information Thomas Loveard
Email: toml@cs.rmit.edu.au

Contact Information Vic Ciesielski
Email: vc@cs.rmit.edu.au
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Referenced by
1 newer article

  1. Song, Andy (2008) Texture Segmentation by Genetic Programming. Evolutionary Computation 16(4)
    [CrossRef]
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