Kernel-Distance-Based Intuitionistic Fuzzy c-Means Clustering Algorithm and Its Application

• Published in: Pattern recognition and image analysis Vol. 29; no. 4; pp. 592 - 597
• Main Authors: Xiangxiao, Lei, Honglin, Ouyang, Lijuan, Xu
• Format: Journal Article
• Language: English
• Published: Moscow Pleiades Publishing 01.10.2019; Springer Nature B.V
• Subjects: Algorithms; Clustering; Computer Science; Euclidean geometry; Fuzzy sets; Image Processing and Computer Vision; Image segmentation; Kernels; Machine vision; Mathematical Theory of Pattern Recognition; Object recognition; Optimization; Pattern Recognition; kernel space; fuzzy c-means clustering; intuitionistic fuzzy set; hesitation degree
• ISSN: 1054-6618; 1555-6212
• DOI: 10.1134/S1054661819040199

Image segmentation plays an important role in machine vision, image recognition, and imaging applications. Based on the fuzzy c-means clustering algorithm, a kernel-distance-based intuitionistic fuzzy c-means clustering (KIFCM) algorithm is proposed. First, a fuzzy complement operator is used to generate the membership degree whereby the hesitation degree of intuitionistic fuzzy set is generated; second, a kernel-induced function is used to calculate the distance from each point to the cluster center instead of the Euclidean distance; third, a new objective function that includes the hesitation degree is established, and the optimization of the objective function results in new iterative expressions for the membership degree and the cluster center. The proposed KIFCM algorithm is compared with the fuzzy c-means clustering (FCM) algorithm, the kernel fuzzy c-means clustering (KFCM) algorithm, and the intuitionistic fuzzy c-means clustering (IFCM) algorithm in segmenting five images. The experimental results verify the effectiveness and superiority of our proposed KIFCM algorithm.