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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 6/2014

01.12.2014 | Original Article

Fuzzy clustering with non-local information for image segmentation

verfasst von: Jingjing Ma, Dayong Tian, Maoguo Gong, Licheng Jiao

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 6/2014

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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.
Vorheriger Artikel A fast template matching-based algorithm for railway bolts detection
Nächster Artikel A comparison of ℓ1-regularizion, PCA, KPCA and ICA for dimensionality reduction in logistic regression

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Metadaten
Titel
Fuzzy clustering with non-local information for image segmentation
verfasst von
Jingjing Ma
Dayong Tian
Maoguo Gong
Licheng Jiao
Publikationsdatum
01.12.2014
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 6/2014
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-014-0227-3

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