A Novel Kernelized Total Bregman Divergence-Driven Possibilistic Fuzzy Clustering With Multiple Information Constraints for Image Segmentation

Abstract

Aiming at the problem that existing robust fuzzy clustering algorithms are still sensitive to high noise, a total Bregman divergence-driven possibilistic fuzzy clustering with multiple information constraints and kernel metric (TSKFLICM) is proposed in this article. First, we introduce total Bregman divergence (TBD) to overcome the shortcoming that Bregman divergence is variant with rotation. Second, polynomial kernel function is used to kernelize TBD, and kernelized TBD is embedded with neighborhood information of pixels to further enhance its ability to suppress noise. Finally, kernelized TBD with spatial information constraints is combined with possibilistic typicality to construct the novel objective function of possibilistic fuzzy clustering, and a TBD-driven kernel possibilistic fuzzy clustering with multiple information constraints is obtained through optimization theory. The effectiveness of the proposed algorithm for noisy image segmentation is explained by means of sample weighted clustering method. Experimental results show that compared with other algorithms, the Jaccard score, segmentation accuracy, and peak signal-to-noise ratio of the proposed algorithm are improved by 0.017–0.481, 1.327%–41.260%, and 2.416–11.765, respectively. Therefore, the TSKFLICM algorithm has better segmentation performance and stronger antinoise ability than the existing state-of-the-art fuzzy clustering algorithm.