A multiobjective spatial fuzzy clustering algorithm for image segmentation

作者：Zhao, Feng^*; Liu, Hanqiang; Fan, Jiulun

来源：Applied Soft Computing, 2015, 30: 48-57.

DOI：10.1016/j.asoc.2015.01.039

• Image segmentation
• Multiobjective optimization
• Fuzzy clustering
• Non-local spatial information
• Cluster validity measure

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.

• 出版日期2015-5
• 单位陕西师范大学; 西安邮电大学