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Knowledge and Information Systems

, Volume 43, Issue 3, pp 583–597 | Cite as

A modified adaptive differential evolution algorithm for color image segmentation

Regular Paper

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.

Keywords

Differential evolution (DE) Segmentation Spatial fuzzy C-mean (sFCM) Archive Cluster center Crossover  Mutation 

Notes

Acknowledgments

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

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Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  1. 1.National University of Computer and Emerging SciencesIslamabadPakistan
  2. 2.College of Computer and Information SciencesAl Imam Mohammad Ibn Saud Islamic University (IMSIU)RiyadhSaudi Arabia
  3. 3.Department of Electronic and Electrical EngineeringUniversity of SheffieldSheffieldUK

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