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Asia-Pacific Conference on Simulated Evolution and Learning

SEAL 2017: Simulated Evolution and Learning pp 499–511Cite as

A Multitree Genetic Programming Representation for Automatically Evolving Texture Image Descriptors

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10593))

Abstract

Image descriptors are very important components in computer vision and pattern recognition that play critical roles in a wide range of applications. The main task of an image descriptor is to automatically detect micro-patterns in an image and generate a feature vector. A domain expert is often needed to undertake the process of developing an image descriptor. However, such an expert, in many cases, is difficult to find or expensive to employ. In this paper, a multitree genetic programming representation is adopted to automatically evolve image descriptors. Unlike existing hand-crafted image descriptors, the proposed method does not rely on predetermined features, instead, it automatically identifies a set of features using a few instances of each class. The performance of the proposed method is assessed using seven benchmark texture classification datasets and compared to seven state-of-the-art methods. The results show that the new method has significantly outperformed its counterpart methods in most cases.

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Notes

  1. 1.

    Available at: http://multibandtexture.recherche.usherbrooke.ca.

  2. 2.

    Available at: http://www.outex.oulu.fi/index.php?page=classification.

  3. 3.

    Available at: http://www.cb.uu.se/~gustaf/KylbergSintornRotation/.

  4. 4.

    Available at: http://www.cb.uu.se/~gustaf/texture/.

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Author information

Authors and Affiliations

  1. School of Engineering and Computer Science, Victoria University of Wellington, P.O. Box 600, Wellington, 6140, New Zealand

    Harith Al-Sahaf, Bing Xue & Mengjie Zhang

Authors
  1. Harith Al-Sahaf
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  2. Bing Xue
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  3. Mengjie Zhang
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Corresponding author

Correspondence to Harith Al-Sahaf .

Editor information

Editors and Affiliations

  1. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  2. City University of Hong Kong, Hong Kong, Kowloon, Hong Kong

    Kay Chen Tan

  3. Victoria University of Wellington, Wellington, Wellington, New Zealand

    Mengjie Zhang

  4. Southern University of Science and Technology, Shenzhen, China

    Ke Tang

  5. RMIT University, Melbourne, Victoria, Australia

    Xiaodong Li

  6. City University of Hong Kong, Kowloon Tong, Hong Kong

    Qingfu Zhang

  7. Peking University, Beijing, China

    Ying Tan

  8. University of Leipzig, Leipzig, Germany

    Martin Middendorf

  9. University of Surrey, Guildford, Surrey, United Kingdom

    Yaochu Jin

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Al-Sahaf, H., Xue, B., Zhang, M. (2017). A Multitree Genetic Programming Representation for Automatically Evolving Texture Image Descriptors. In: Shi, Y., et al. Simulated Evolution and Learning. SEAL 2017. Lecture Notes in Computer Science(), vol 10593. Springer, Cham. https://doi.org/10.1007/978-3-319-68759-9_41

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  • DOI: https://doi.org/10.1007/978-3-319-68759-9_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68758-2

  • Online ISBN: 978-3-319-68759-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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