Semisupervised Approach to Surrogate-Assisted Multiobjective Kernel Intuitionistic Fuzzy Clustering Algorithm for Color Image Segmentation

Multiobjective evolutionary algorithms (MOEAs) are effective optimization methods. To improve the segmentation performance and time efficiency of MOEAs-based fuzzy clustering algorithms for color images, a semisupervised surrogate-assisted multiobjective kernel intuitionistic fuzzy clustering (S3MKIFC) algorithm is proposed in this article. The main contributions of S3MKIFC can be summarized as follows: 1) semisupervised kernel intuitionistic fuzzy objective functions are constructed for optimization to search satisfactory segmentation results; 2) to reduce the computational cost, the Kriging model is used to predict the values of objective functions instead of directly calculating the expensive objective functions; 3) a semisupervised selection strategy and a semisupervised model management mechanism are proposed to balance the convergence and diversity and improve the predicted accuracy of the Kriging model, respectively; and 4) a novel semisupervised kernel intuitionistic fuzzy cluster validity index is defined to select the optimal solution from the final nondominated solution set. Experimental results on two color image libraries demonstrate that S3MKIFC outperforms state-of-the-art methods in segmentation performance and meanwhile possesses a low time cost.