Robust Possibilistic Fuzzy Additive Partition Clustering Motivated by Deep Local Information

Abstract

Aiming at the weak robustness of possibilistic fuzzy clustering against noise, a robust possibilistic fuzzy additive partition clustering with master–slave neighborhood information constraints is proposed for high noise image segmentation. This algorithm first constructs a master–slave neighborhood model, which consists of the master neighborhood window of the current pixel and the slave neighborhood window around the master neighborhood pixel. Then, the master–slave neighborhood information is integrated into the possibilistic fuzzy additive partition clustering model, and a novel robust possibilistic fuzzy clustering model incorporating deep local information is constructed. Next, this clustering model is further simplified by Cauchy inequality and a robust master–slave neighborhood information-driven possibilistic fuzzy clustering algorithm is derived by optimization theory. Extensive experimental results indicate that the proposed algorithm is very effective for noisy image segmentation, and its segmentation performance is significantly better than many existing state-of-the-art fuzzy clustering-related algorithms. In short, the work of this paper has profound significance for the development of robust fuzzy clustering theory.