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International Conference on Computer Vision Systems

ICVS 2008: Computer Vision Systems pp 131–140Cite as

Feature Extraction and Classification by Genetic Programming

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5008))

Abstract

This paper explores the use of genetic programming for constructing vision systems. A two-stage approach is used, with separate evolution of the feature extraction and classification stages. The strategy taken for the classifier is to evolve a set of partial solutions, each of which works for a single class. It is found that this approach is significantly faster than conventional genetic programming, and frequently results in a better classifier. The effectiveness of the approach is explored on three classification problems.

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Authors and Affiliations

  1. VASE Laboratory, Department of Computing and Electronic Systems, University of Essex, Colchester, CO4 3SQ, UK

    Olly Oechsle & Adrian F. Clark

Authors
  1. Olly Oechsle
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  2. Adrian F. Clark
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Editor information

Antonios Gasteratos Markus Vincze John K. Tsotsos

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© 2008 Springer-Verlag Berlin Heidelberg

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Oechsle, O., Clark, A.F. (2008). Feature Extraction and Classification by Genetic Programming. In: Gasteratos, A., Vincze, M., Tsotsos, J.K. (eds) Computer Vision Systems. ICVS 2008. Lecture Notes in Computer Science, vol 5008. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79547-6_13

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  • DOI: https://doi.org/10.1007/978-3-540-79547-6_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-79546-9

  • Online ISBN: 978-3-540-79547-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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