Fuzzy C-Means Clustering with Fast and Adaptive Non-local Spatial Constraint and Membership Linking for Noise Image Segmentation

Xiaopeng WANG; Qingsheng WANG; Jianjun JIAO; Jincheng LIANG

doi:10.11999/JEIT191016

Volume 43 Issue 1

Jan. 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(1): 171-178

ZHOU Yulong, CAO Xiangyu, GAO Jun, ZHENG Yuejun, ZHANG Chen. Dualband Frequncey Selective Surface and Its Application to Wideband RCS Reduction of The Microstrip Antenna[J]. Journal of Electronics & Information Technology, 2017, 39(6): 1446-1451. doi: 10.11999/JEIT160854

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Xiaopeng WANG, Qingsheng WANG, Jianjun JIAO, Jincheng LIANG. Fuzzy C-Means Clustering with Fast and Adaptive Non-local Spatial Constraint and Membership Linking for Noise Image Segmentation[J]. Journal of Electronics & Information Technology, 2021, 43(1): 171-178. doi: 10.11999/JEIT191016

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Fuzzy C-Means Clustering with Fast and Adaptive Non-local Spatial Constraint and Membership Linking for Noise Image Segmentation

doi: 10.11999/JEIT191016

School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China

Funds: The National Natural Science Foundation of China (61761027)

Received Date: 2019-12-19
Rev Recd Date: 2020-11-04

Available Online: 2020-11-07

Publish Date: 2021-01-15

Abstract

Abstract

Considering the problem of the low anti-noise performance when Fuzzy C-Means clustering (FCM) algorithm is applied to image segmentation, a FCM clustering algorithm with fast and adaptive non-local spatial constraint and membership linking is proposed in this paper. Firstly, in order to increase the computing speed of non-local spatial term, a fast method is proposed by modifying the loop based on all pixels in an image into a loop based on search window and by utilising spatial shift image and recursive Gaussian filter. Next, the squared difference between original image and non-local spatial term is calculated as adaptive weight of non-local information term. The squared difference is reciprocally transformed as adaptive weight of the original image. Finally, the membership linking is established to reduce the iteration steps before convergence by adding the square of the sum of all the membership degrees in every cluster in logarithmic form as the denominator of the objectvie function. Experiments on noisy artificial and natural images prove that this proposed algorithm has superior performance in terms of Segmentation accuracy, mean intersection over union, normalized mutual information, running time and iteration steps.
- Noisy Image Segmentation,
- Fuzzy C-Means clustering (FCM),
- Non-local spatial information,
- Adaptive constraint,
- Membership linking

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References(22)

References

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