Intuitionistic fuzzy entropy clustering algorithm for infrared image segmentation

Article type: Research Article

Authors: Zhou, Xiaoguang^* | Zhao, Renhou | Yu, Fengquan | Tian, Huaiying

Affiliations: Department of Flight Simulation and Training, Naval Aviation Institute, Liaoning Huludao, China

Correspondence: [*] Corresponding author. Xiaoguang Zhou, Department of Flight Simulation and Training, Naval Aviation Institute, Liaoning Huludao 125001, China. Tel.: +86 15898227885; E-mail: zhouxiaoguanghf@163.com.

Abstract: In order to discover the detailed information contained in the infrared image, this paper proposes an intuitionistic fuzzy entropy clustering algorithm for image segmentation. Because of the blurred characteristic of the infrared image, the intuitionistic fuzzy sets are selected for infrared image segmentation. The object function of the intuitionistic fuzzy entropy clustering algorithm is constructed by the intuitionistic fuzzy distance and the intuitionistic fuzzy entropy based on the regularization technique. The condition of the ntuitionistic fuzzy entropy clustering algorithm is researched. The Lagrange multiplier method is employed to calculate membership functions and the centroids. An iterative algorithm is deduced to calculate Lagrange multiplier coefficient and membership. Finally, experimental results demonstrate the ability of the intuitionistic fuzzy entropy clustering algorithm for infrared image segmentation.

Keywords: Intuitionistic fuzzy C-means clustering, infrared image segmentation, membership degree

DOI: 10.3233/IFS-151894

Journal: Journal of Intelligent & Fuzzy Systems, vol. 30, no. 3, pp. 1831-1840, 2016