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Multi-objective Genetic Programming for Figure-Ground Image Segmentation

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Artificial Life and Computational Intelligence (ACALCI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9592))

Abstract

Figure-ground segmentation is a crucial preprocessing step in areas of computer vision and image processing. As an evolutionary computation technique, genetic programming (GP) can evolve algorithms automatically for complex problems and has been introduced for image segmentation. However, GP-based methods face a challenge to control the complexity of evolved solutions. In this paper, we develop a novel exponential function to measure the solution complexity. This complexity measure is utilized as a fitness evaluation measure in GP in two ways: one method is to combine it with the classification accuracy linearly to form a weighted sum fitness function; the other is to treat them separately as two objectives. Based on this, we propose a weighted sum GP method and a multi-objective GP (MOGP) method for segmentation tasks. We select four types of test images from bitmap, Brodatz texture, Weizmann and PASCAL databases. The proposed methods are compared with a reference GP method, which is single-objective (the classification accuracy) without considering the solution complexity. The results show that the new approaches, especially MOGP, can significantly reduce the solution complexity and the training time without decreasing the segmentation performance.

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Authors and Affiliations

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

    Yuyu Liang, Mengjie Zhang & Will N. Browne

Authors
  1. Yuyu Liang
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  2. Mengjie Zhang
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  3. Will N. Browne
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Corresponding author

Correspondence to Yuyu Liang .

Editor information

Editors and Affiliations

  1. Canberra, Aust Capital Terr, Australia

    Tapabrata Ray

  2. University of New South Wales, Canberra, Aust Capital Terr, Australia

    Ruhul Sarker

  3. RMIT University, Melbourne, Victoria, Australia

    Xiaodong Li

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Liang, Y., Zhang, M., Browne, W.N. (2016). Multi-objective Genetic Programming for Figure-Ground Image Segmentation. In: Ray, T., Sarker, R., Li, X. (eds) Artificial Life and Computational Intelligence. ACALCI 2016. Lecture Notes in Computer Science(), vol 9592. Springer, Cham. https://doi.org/10.1007/978-3-319-28270-1_12

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  • DOI: https://doi.org/10.1007/978-3-319-28270-1_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28269-5

  • Online ISBN: 978-3-319-28270-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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