Abstract
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.
Highlights
Fuzzy C-means (FCM) [1] is an unsupervised clustering method, which is widely used in numerous applications
Many researchers [3,4,5,6,7] proposed a series of improved fast robust FCM algorithms using local and nonlocal filtered information, sparse reconstruction information of neighborhood window. e shortcoming of these robust FCM algorithms is that they cannot automatically determine spatial information constraint parameters
Wang et al [9] put forward a robust fuzzy clustering segmentation algorithm with local and nonlocal spatial constraints; its weighted constraint parameter is constructed by Gaussian function of gray information deviation between current central pixel and its neighborhood pixels
Summary
Fuzzy C-means (FCM) [1] is an unsupervised clustering method, which is widely used in numerous applications. Robust fuzzy clustering algorithms with spatial gray information and fuzzy membership constraints enhance the ability to suppress noise or singular data, they still have some shortcomings, including the sensitivity to initial values and constraints in achieving local optimal solutions. E semisupervised fuzzy clustering algorithm has been successfully applied in the data analysis [37,38,39], shape annotation [40], remote sensing image segmentation [41, 42], and the image change detection [43] Based on these studies, Pedrycz and Waletzky [44] multiplied the supervision information of the prior classification labels by a Boolean vector to distinguish whether the sample is supervised or not. In order to evaluate the performance of the proposed algorithm, segmentation tests of different types of noisy image of synthetic, standard, medical, and remote sensing images are performed
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