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Fuzzy clustering with non-local information for image segmentation

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Abstract

Fuzzy c-means (FCM) algorithms have been shown effective for image segmentation. A series of enhanced FCM algorithms incorporating spatial information have been developed for reducing the effect of noises. This paper presents a robust FCM algorithm with non-local spatial information for image segmentation, termed as NLFCM. It incorporates two factors: one is the local similarity measure depending on the differences between the central pixel and its neighboring pixels in the image; the other is the non-local similarity measure depended on all pixels whose neighborhood configurations are similar to their neighborhood pixels. Furthermore, an adaptive weight is introduced to control the trade-off between local similarity measure and non-local similarity measure. The experimental results on synthetic images and real images under different types of noises show that the new algorithm is effective, and they are relatively independent to the types of noises.

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Acknowledgments

The authors wish to thank the editors and anonymous reviewers for their valuable comments and helpful suggestions which greatly improved the paper’s quality. This work was supported by the National Natural Science Foundation of China (Grant Nos. 61273317, 61202176, 61203303), the National Top Youth Talents Program of China, the Specialized Research Fund for the Doctoral Program of Higher Education (Grant No. 20130203110011), and the Fundamental Research Fund for the Central Universities (Grant Nos. K50510020001 and K5051202053).

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Authors and Affiliations

  1. Key Lab of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, No.2 South TaiBai Road, Mailbox 224, Xi’an, 710071, China

    Jingjing Ma, Dayong Tian, Maoguo Gong & Licheng Jiao

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  1. Jingjing Ma
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  2. Dayong Tian
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  3. Maoguo Gong
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  4. Licheng Jiao
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Correspondence to Maoguo Gong.

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Ma, J., Tian, D., Gong, M. et al. Fuzzy clustering with non-local information for image segmentation. Int. J. Mach. Learn. & Cyber. 5, 845–859 (2014). https://doi.org/10.1007/s13042-014-0227-3

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