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Towards Genetic Programming for Texture Classification

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Book cover AI 2001: Advances in Artificial Intelligence (AI 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2256))

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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.

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Author information

Authors and Affiliations

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

    Andy Song, Thomas Loveard & Vic Ciesielski

Authors
  1. Andy Song
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  2. Thomas Loveard
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  3. Vic Ciesielski
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Editor information

Editors and Affiliations

  1. School of Computer and Information Science, University of South Australia, Mawson Lakes, 5095, SA, Australia

    Markus Stumptner  & Dan Corbett  & 

  2. Department of Computer Science, University of Adelaide, 5001, Adelaide, SA, Australia

    Mike Brooks

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© 2001 Springer-Verlag Berlin Heidelberg

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Song, A., Loveard, T., Ciesielski, V. (2001). Towards Genetic Programming for Texture Classification. In: Stumptner, M., Corbett, D., Brooks, M. (eds) AI 2001: Advances in Artificial Intelligence. AI 2001. Lecture Notes in Computer Science(), vol 2256. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45656-2_40

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  • DOI: https://doi.org/10.1007/3-540-45656-2_40

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42960-9

  • Online ISBN: 978-3-540-45656-8

  • eBook Packages: Springer Book Archive

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