A Robust FCM Algorithm Combining Non-Local Spatial Information and KL Information
Aiming at the problem that traditional Fuzzy C-Means(FCM)clustering algorithm is sensitive to noise,a robust FCM algorithm combining non-local spatial information and KL information is proposed.Firstly,the gray-scale information is fused with non-local spatial information to enhance the robustness of the algorithm against noise.Secondly,KL information is introduced into the objective function to reduce the ambiguity of segmentation.Under the mixed noise condition of 5％density,the experimental results of synthesized image and natural image show that the algorithm has high segmentation accuracy and robustness,and can divide noise image better.
Fuzzy C-MeansImage segmentationNon-local spatial informationKL information
NETL
NSTL
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