Image Segmentation Algorithm Based on Context Fuzzy C-Means Clustering

Jindong XU; Tianyu ZHAO; Guozheng FENG; Shifeng OU

doi:10.11999/JEIT200263

Volume 43 Issue 7

Jul. 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(7): 2079-2086

Jindong XU, Tianyu ZHAO, Guozheng FENG, Shifeng OU. Image Segmentation Algorithm Based on Context Fuzzy C-Means Clustering[J]. Journal of Electronics & Information Technology, 2021, 43(7): 2079-2086. doi: 10.11999/JEIT200263

Citation:

Jindong XU, Tianyu ZHAO, Guozheng FENG, Shifeng OU. Image Segmentation Algorithm Based on Context Fuzzy C-Means Clustering[J]. Journal of Electronics & Information Technology, 2021, 43(7): 2079-2086. doi: 10.11999/JEIT200263

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Image Segmentation Algorithm Based on Context Fuzzy C-Means Clustering

doi: 10.11999/JEIT200263

1.
School of Computer and Control Engineering, Yantai University, Yantai 264005, China
2.
School of Opto-Electronic Information Science and Technology, Yantai University, Yantai 264005, China

Funds: The National Natural Science Foundation of China (62072391, 62066013), The Natural Science Foundation of Shandong Province (ZR2019MF060, ZR2017MF008), The Project of Shandong Province Higher Educational Science and Technology Key Program (J18KZ016), The Yantai Science and Technology Plan (2018YT06000271)

Received Date: 2020-04-10
Rev Recd Date: 2020-10-23

Available Online: 2021-03-30

Publish Date: 2021-07-10

Abstract

Abstract

The correlation information between pixels is of great significance for image segmentation. The existing Fuzzy C-Means (FCM) clustering algorithm lacks sufficient consideration for it. Based on the reliability measure of spatial context, this paper proposes a Reliability-based Spatial context Fuzzy C-Means (RSFCM) clustering algorithm: The clustering algorithm anti-noise performance is improved by effectively modeling the spatial neighborhood; A new reliability fuzzy metric is proposed, which balances the relationship between detail retention and anti-noise, so that the clustering results are more accurate. A synthetic image, a traffic sign image and a remote sensing image are used to test the algorithms performance. The results show, compared with the existing FCM algorithm, RSFCM can effectively suppress heterogeneity of intra-class objects caused by Salt & Pepper noise and Gaussian noise for the image segmentation, improve pixels separability and preserve the edge details of the image greatly.
- Image segmentation,
- Clustering,
- Fuzzy C-Means (FCM),
- Spatial context

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References(21)

References

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