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To improve the segmentation performance and anti-noise robustness of existing weighted kernel intuitionistic fuzzy clustering, a robust kernelized total Bregman divergence-based fuzzy local information clustering motivated by intuitionistic fuzzy information is proposed. In this algorithm, a kernelized total Bregman divergence is extended by a polynomial kernel function, and the corresponding intuitionistic kernelized total Bregman divergence is put forward to measure the difference between intuitionistic fuzzy sets. Then, the weighted local information is introduced into the objective function of intuitionistic fuzzy clustering, and the similarity between the current pixel and its neighborhood pixels is constructed to better describe the influence of neighborhood pixels on the current pixel. Finally, the square root of the deviation between the current pixel and the mean value of its neighboring pixels is used to adjust the local spatial information to improve the robustness to noise or outliers and further enhance the anti-noise ability of the algorithm. Experimental results show that the proposed algorithm has better clustering performance and stronger anti-noise robustness than existing state-of-the-art fuzzy clustering-related algorithms. [ABSTRACT FROM AUTHOR]

To improve the effectiveness and robustness of the existing semisupervised fuzzy clustering for segmenting image corrupted by noise, a kernel space semisupervised fuzzy C-means clustering segmentation algorithm combining utilizing neighborhood spatial gray information with fuzzy membership information is proposed in this paper. The mean intensity information of neighborhood window is embedded into the objective function of the existing semisupervised fuzzy C-means clustering, and the Lagrange multiplier method is used to obtain its iterative expression corresponding to the iterative solution of the optimization problem. Meanwhile, the local Gaussian kernel function is used to map the pixel samples from the Euclidean space to the high-dimensional feature space so that the cluster adaptability to different types of image segmentation is enhanced. Experiment results performed on different types of noisy images indicate that the proposed segmentation algorithm can achieve better segmentation performance than the existing typical robust fuzzy clustering algorithms and significantly enhance the antinoise performance. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Gaussian kernel is defined by Euclidean distance and has been widely used in many fields. In the view of Euclidean distance is sensitive to outliers or noise and it is difficult to obtain satisfactory results for complex non-convex data. Entropy-like divergence is firstly induced by combining Jenson-Shannon/Bregman divergence with convex function, and its mercer kernel function called entropy-like divergence-based kernel is also constructed in this paper. Secondly, a new fuzzy weighted factor based on entropy-like divergence-based kernel is proposed by improving the existing trade-off weighting factor of kernel-based fuzzy local information C-means clustering (KWFLICM). In the end, a weighted fuzzy local information clustering based on entropy-like divergence-based kernel (EKWFLICM) is presented, which combines a new weighted fuzzy factor and entropy-like divergence-based kernel. Experiment results show that the proposed algorithm outperforms the segmentation performance of existing state-of-the-art fuzzy clustering-related algorithms for the image in presence of high noise. • An entropy-like divergence and its mercer kernel function are presented. • A novel divergence-based kernel fuzzy clustering is proved to be convergent. • A kernel weighted fuzzy clustering based on entropy-like divergence is constructed. • Proposed algorithm outperforms existing KWFLICM and ILKFCM for image segmentation. [ABSTRACT FROM AUTHOR]