Total Bregman divergence-driven possibilistic fuzzy clustering with kernel metric and local information for grayscale image segmentation

Abstract

Kernel possibilistic fuzzy C-means with local information (KWPFLICM) has important research significance of image segmentation, but it is very sensitive to high noise or outliers. To enhance the segmentation performance of the algorithm, this paper proposes a kernelized total Bregman divergence-driven possibilistic fuzzy clustering with local information (TKWPFLICM). Firstly, a polynomial kernel function is introduced to kernelize total Bregman divergence (TBD), and local neighborhood information of the pixel is used to modify it, which overcomes the shortcomings of Bregman divergence (BD) with rotation variability; Secondly, the modified kernelized TBD and possibilistic typicality are combined to further enhance the anti-noise ability of the algorithm; Finally, the modified kernelized TBD is introduced into the objective function of KWPFLICM algorithm, then a novel robust fuzzy clustering algorithm is derived by optimization theory. Experimental results show that compared with existing fuzzy clustering-related algorithms, the average SA improvement on TKWPFLICM algorithm is in the range of 0.791% to 33.237%. Therefore, TKWPFLICM algorithm has better anti-noise robustness and segmentation accuracy.