Affiliations: [a] Key Laboratory of Electronic Information Application Technology for Scene Investigation, Ministry of Public Security, Xi’an, Shaanxi, China | [b] School of Communications and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an, Shaanxi, China | [c] School of Computer Science, Shaanxi Normal University, Xi’an, Shaanxi, China
Correspondence:
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Corresponding author: Hanqiang Liu, School of Computer Science, Shaanxi Normal University, No. 620, West Chang’an Avenue, Chang’an District, Xi’an, Shaanxi 710119, China. Tel.:+86 29 85310161; Fax: +86 29 85310661; E-mail: liuhq@snnu.edu.cn.
Abstract: The concept of intuitionistic fuzzy set has been found to be highly useful to handle vagueness in data. Based on intuitionistic fuzzy set theory, intuitionistic fuzzy clustering algorithms are proposed and play an important role in image segmentation. However, due to the influence of initialization and the presence of noise in the image, intuitionistic fuzzy clustering algorithm cannot acquire the satisfying performance when applied to segment images corrupted by noise. In order to solve above problems, a robust intuitionistic fuzzy clustering with bias field estimation (RIFCB) is proposed for noisy image segmentation in this paper. Firstly, a noise robust intuitionistic fuzzy set is constructed to represent the image by using the neighboring information of pixels. Then, initial cluster centers in RIFCB are adaptively determined by utilizing the frequency statistics of gray level in the image. In addition, in order to offset the information loss of the image when constructing the intuitionistic fuzzy set of the image, a new objective function incorporating a bias field is designed in RIFCB. Based on the new initialization strategy, the intuitionistic fuzzy set representation, and the incorporation of bias field, the proposed method preserves the image details and is insensitive to noise. Experimental results on some Berkeley images show that the proposed method achieves satisfactory segmentation results on images corrupted by different kinds of noise in contrast to conventional fuzzy clustering algorithms.
