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 e...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.展开更多
基金supported by the Fund for Foreign Scholars in University Research and Teaching Programs(the 111 Project)(Grant No.B07048)the Program for New Century Excellent Talents in University(Grant No.NCET-08-0811)+2 种基金the National Natural Science Foundation of China(Grant No.61203303)the Natural Science Basic Research Plan in Shaanxi Province of China(Grant No.2010JQ8023)the Fundamental Research Funds for the Central Universities(Grant No.K50510020011).
文摘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.
