Robust Superpixel-Based Fuzzy Possibilistic Clustering Method Incorporating Local Information for Image Segmentation

In recent years, several superpixel-segmentation methods have been developed to efficiently segment noisy images. However, these methods still face challenges such as high computational complexity and poor adaptability. Therefore, this paper develops a novel superpixel-based robust segmentation model including two modules: superpixel generation and superpixel-based image segmentation. In the superpixel generation module, a fuzzy factor containing local spatial information of pixels is introduced into fuzzy possibilistic clustering algorithm with local search. In the superpixel-based segmentation module, a superpixel-based fuzzy C-means algorithm with local spatial information of superpixels is proposed, which nonlinearly combines the membership of superpixels with the membership of their neighboring superpixels. The experimental results demonstrate that the proposed method outperforms existing state-of-the-art segmentation algorithms in terms of evaluation indexes and visual effects.