A multiobjective spatial fuzzy clustering algorithm for image segmentation |
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| Affiliation: | 1. School of Telecommunication and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an, PR China;2. School of Computer Science, Shaanxi Normal University, Xi’an, PR China;1. Centre for Biomedical Engineering, Transportation Research Alliance, Universiti Teknologi Malaysia, Skudai, Malaysia;2. Faculty of Bioscience and Medical Engineering, Universiti Teknologi Malaysia, Skudai, Malaysia;1. College of Mathematics, Physics and Information Engineering, Jiaxing University, Jiaxing 314001, China;2. College of Engineering, Shaoxing University, Shaoxing 312000, China;1. School of Electrical Engineering and Automation, Tianjin Polytechnic University, Tianjin 300387, China;2. Tianjin Key Laboratory of Advanced Technology of Electrical Engineering and Energy, Tianjin 300387, China;1. College of Computer Science, Shaanxi Normal University, Xi’an 710062, China;2. School of Computer Science and Technology, Xidian University, Xi’an 710071, China;1. Department of Computer Engineering, Electrical Engineering and Applied Mathematics, University of Salerno, 84084 Fisciano, Italy;2. CORISA, Department of Computer Science, University of Salerno, 84084 Fisciano, Italy |
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| Abstract: | This article describes a multiobjective spatial fuzzy clustering algorithm for image segmentation. To obtain satisfactory segmentation performance for noisy images, the proposed method introduces the non-local spatial information derived from the image into fitness functions which respectively consider the global fuzzy compactness and fuzzy separation among the clusters. After producing the set of non-dominated solutions, the final clustering solution is chosen by a cluster validity index utilizing the non-local spatial information. Moreover, to automatically evolve the number of clusters in the proposed method, a real-coded variable string length technique is used to encode the cluster centers in the chromosomes. The proposed method is applied to synthetic and real images contaminated by noise and compared with k-means, fuzzy c-means, two fuzzy c-means clustering algorithms with spatial information and a multiobjective variable string length genetic fuzzy clustering algorithm. The experimental results show that the proposed method behaves well in evolving the number of clusters and obtaining satisfactory performance on noisy image segmentation. |
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| Keywords: | Image segmentation Multiobjective optimization Fuzzy clustering Non-local spatial information Cluster validity measure |
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