Kernel-Based Intuitionistic Fuzzy Clustering Image Segmentation Based on Grey Wolf Optimizer with Differential Mutation

Abstract

Conventional fuzzy clustering algorithms present several disadvantages with respect to image segmentation, including a tendency to arrive at local optima and a relatively high sensitivity to noise and initial cluster centers. To address these issues, we herein propose a kernel-based intuitionistic fuzzy clustering approach combining an improved grey wolf optimizer with a kernel-based intuitionistic fuzzy C-means clustering (IGWO-KIFCM) algorithm capable of carrying out differential mutations for image segmentation. The proposed method extracts spatial information from images and then applies a kernel-based intuitionistic fuzzy clustering objective function to improve the robustness of the algorithm against noise. To cope with the initial sensitivity and local optima issues, we develop an improved grey wolf optimizer based on differential mutation for the global optimization of the cluster centers. A comparative optimization assessment using six classic functions reveals that the improved grey wolf optimizer algorithm outperforms both the grey wolf optimizer and mean grey wolf optimizer algorithms in terms of searching ability and does not easily run into local optima. Moreover, the IGWO-KIFCM algorithm surpasses several other algorithms with respect to clustering performance across multiple datasets, and achieves good results in segmenting images with various types of noises.