Robust Semisupervised Kernelized Fuzzy Local Information C-Means Clustering for Image Segmentation

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.

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