A modified adaptive differential evolution algorithm for color image segmentation

Ahmad Khan, M. Arfan Jaffar, Ling Shao

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)583-597
JournalKnowledge and Information Systems
Issue number3
Publication statusPublished - 1 Jun 2015


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