Affiliations: [a] Key Laboratory of Electronic Information Application Technology for Scene Investigation, Ministry of Public Security, Xi’an, China
| [b] School of Communications and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an, China
| [c] School of Computer Science, Shaanxi Normal University, Xi’an, China
Correspondence:
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Corresponding author. Jiulun Fan, Xi’an University of Posts and Telecommunications, Xi’an, China. Tel.: +86 13002985988; E-mail: jiulunf@xupt.edu.cn.
Abstract: Interval valued fuzzy c-means (IVFCM) clustering algorithm is one of effective clustering algorithms. When applied to image segmentation, IVFCM includes three problems as follows: (1) It is sensitive to the initial values of algorithm and may easily fall into the local optimal. (2) The algorithm is sensitive to the image noise and cannot obtain the satisfying performance on images corrupted by noise. (3) It always performs image segmentation under one objective function, therefore it cannot meet multiple practical needs. In order to address these problems, a multi-objective interval valued fuzzy clustering algorithm is proposed in this paper. This method constructs two novel interval valued fuzzy fitness functions which utilize the non-local spatial information of the image. Then a new mutation operator combining the interval valued fuzzy information of image is designed. Furthermore, an effective interval valued fuzzy cluster validity index using the non-local spatial information of image is presented to select a single solution from the non-dominated solution set. Experimental results show that the proposed method behaves well in noisy image segmentation.
