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Image segmentation based on differential immune clone clustering algorithm

Image segmentation based on differential immune clone clustering algorithm Purpose – The purpose of this paper is to present a Differential Immune Clone Clustering Algorithm (DICCA) to solve image segmentation. Design/methodology/approach – DICCA combines immune clone selection and differential evolution, and two populations are used in the evolutionary process. Clone reproduction and selection, differential mutation, crossover and selection are adopted to evolve two populations, which can increase population diversity and avoid local optimum. After extracting the texture features of an image and encoding them with real numbers, DICCA is used to partition these features, and the final segmentation result is obtained. Findings – This approach is applied to segment all sorts of images into homogeneous regions, including artificial synthetic texture images, natural images and remote sensing images, and the experimental results show the effectiveness of the proposed algorithm. Originality/value – The method presented in this paper represents a new approach to solving clustering problems. The novel method applies the idea two populations are used in the evolutionary process. The proposed clustering algorithm is shown to be effective in solving image segmentation. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Intelligent Computing and Cybernetics Emerald Publishing

Image segmentation based on differential immune clone clustering algorithm

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Publisher
Emerald Publishing
Copyright
Copyright © 2013 Emerald Group Publishing Limited. All rights reserved.
ISSN
1756-378X
DOI
10.1108/17563781311301535
Publisher site
See Article on Publisher Site

Abstract

Purpose – The purpose of this paper is to present a Differential Immune Clone Clustering Algorithm (DICCA) to solve image segmentation. Design/methodology/approach – DICCA combines immune clone selection and differential evolution, and two populations are used in the evolutionary process. Clone reproduction and selection, differential mutation, crossover and selection are adopted to evolve two populations, which can increase population diversity and avoid local optimum. After extracting the texture features of an image and encoding them with real numbers, DICCA is used to partition these features, and the final segmentation result is obtained. Findings – This approach is applied to segment all sorts of images into homogeneous regions, including artificial synthetic texture images, natural images and remote sensing images, and the experimental results show the effectiveness of the proposed algorithm. Originality/value – The method presented in this paper represents a new approach to solving clustering problems. The novel method applies the idea two populations are used in the evolutionary process. The proposed clustering algorithm is shown to be effective in solving image segmentation.

Journal

International Journal of Intelligent Computing and CyberneticsEmerald Publishing

Published: Mar 22, 2013

Keywords: Differential evolution; Clone selection; Clustering; Image segmentation; Image processing; Cluster analysis

References