A modified adaptive differential evolution algorithm for color image segmentation

Khan, Ahmad, Jaffar, M. Arfan and Shao, Ling (2015) A modified adaptive differential evolution algorithm for color image segmentation. Knowledge and Information Systems, 43 (3). pp. 583-597. ISSN 0219-1377

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Official URL: http://dx.doi.org/10.1007/s10115-014-0741-3


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.

Item Type: Article
Uncontrolled Keywords: Differential evolution (DE), Segmentation, Spatial fuzzy C-mean (sFCM), Archive, Cluster center, Crossover, Mutation
Subjects: G400 Computer Science
G700 Artificial Intelligence
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Related URLs:
Depositing User: Paul Burns
Date Deposited: 10 Jun 2015 10:30
Last Modified: 12 Oct 2019 22:30
URI: http://nrl.northumbria.ac.uk/id/eprint/22815

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