Kernel Fuzzy Similarity Measure-Based Spectral Clustering for Image Segmentation

Abstract

Spectral clustering has been successfully used in the field of pattern recognition and image processing. The efficiency of spectral clustering, however, depends heavily on the similarity measure adopted. A widely used similarity measure is the Gaussian kernel function where Euclidean distance is used. Unfortunately, the Gaussian kernel function is parameter sensitive and the Euclidean distance is usually not suitable to the complex distribution data. In this paper, a novel similarity measure called kernel fuzzy similarity measure is proposed first, Then this novel measure is integrated into spectral clustering to get a new clustering method: kernel fuzzy similarity based spectral clustering (KFSC). To alleviate the computational complexity of KFSC on image segmentation, Nystr\(\ddot{o}\)m method is used in KFSC. At last, the experiments on three synthetic texture images are made, and the results demonstrate the effectiveness of the proposed algorithm.