Journal
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
Volume 5, Issue 6, Pages 845-859Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s13042-014-0227-3
Keywords
Fuzzy clustering; Image segmentation; Non-local information; Spatial information
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Funding
- National Natural Science Foundation of China [61273317, 61202176, 61203303]
- National Top Youth Talents Program of China
- Specialized Research Fund for the Doctoral Program of Higher Education [20130203110011]
- Fundamental Research Fund for the Central Universities [K50510020001, K5051202053]
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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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