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A modified adaptive differential evolution algorithm for color image segmentation

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Abstract

Image segmentation is an important low-level vision task. It is a perceptual grouping of pixels based on some similarity criteria. In this paper, a new differential evolution (DE) algorithm, modified adaptive differential evolution, is proposed for color image segmentation. The DE/current-to-pbest mutation strategy with optional external archive and opposition-based learning are used to diversify the search space and expedite the convergence process. Control parameters are automatically updated to appropriate values in order to avoid user intervention of parameters setting. To find an optimal number of clusters (the number of regions or segments), the average ratio of fuzzy overlap and fuzzy separation is used as a cluster validity index. The results demonstrate that the proposed technique outperforms state-of-the-art methods.

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Acknowledgments

We thank anonymous reviewers for their very useful comments and suggestions.

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Authors and Affiliations

  1. National University of Computer and Emerging Sciences, H-11/4, Islamabad, Pakistan

    Ahmad Khan

  2. College of Computer and Information Sciences, Al Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia

    M. Arfan Jaffar

  3. Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield, UK

    Ahmad Khan & Ling Shao

Authors
  1. Ahmad Khan
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  2. M. Arfan Jaffar
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  3. Ling Shao
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Correspondence to M. Arfan Jaffar.

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Khan, A., Jaffar, M.A. & Shao, L. A modified adaptive differential evolution algorithm for color image segmentation. Knowl Inf Syst 43, 583–597 (2015). https://doi.org/10.1007/s10115-014-0741-3

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  • DOI: https://doi.org/10.1007/s10115-014-0741-3

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