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European Conference on the Applications of Evolutionary Computation

EvoApplications 2014: Applications of Evolutionary Computation pp 451–463Cite as

Is a Single Image Sufficient for Evolving Edge Features by Genetic Programming?

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8602))

Abstract

Typically, a single natural image is not sufficient to train a program to extract edge features in edge detection when only training images and their ground truth are provided. However, a single training image might be considered as proper training data when domain knowledge, such as used in Gaussian-based edge detection, is provided. In this paper, we employ Genetic Programming (GP) to automatically evolve Gaussian-based edge detectors to extract edge features based on training data consisting of a single image only. The results show that a single image with a high proportion of true edge points can be used to train edge detectors which are not significantly different from rotation invariant surround suppression. When the programs separately evolved from eight single images are considered as weak classifiers, the combinations of these programs perform better than rotation invariant surround suppression.

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Author information

Authors and Affiliations

  1. School of Mathematics, Statistics and Operations Research, Victoria University of Wellington, P.O. Box 600, Wellington, New Zealand

    Wenlong Fu & Mark Johnston

  2. School of Engineering and Computer Science, Victoria University of Wellington, P.O. Box 600, Wellington, New Zealand

    Mengjie Zhang

Authors
  1. Wenlong Fu
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  2. Mark Johnston
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  3. Mengjie Zhang
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Corresponding author

Correspondence to Wenlong Fu .

Editor information

Editors and Affiliations

  1. Depto. Estadística e Investigación, Universidad Politécnica de Valencia, Valencia, Spain

    Anna I. Esparcia-Alcázar

  2. University of Granada, Granada, Spain

    Antonio M. Mora

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Fu, W., Johnston, M., Zhang, M. (2014). Is a Single Image Sufficient for Evolving Edge Features by Genetic Programming?. In: Esparcia-Alcázar, A., Mora, A. (eds) Applications of Evolutionary Computation. EvoApplications 2014. Lecture Notes in Computer Science(), vol 8602. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45523-4_37

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  • DOI: https://doi.org/10.1007/978-3-662-45523-4_37

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-45522-7

  • Online ISBN: 978-3-662-45523-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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