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Fuzzy C-Means Clustering With Local Information and Kernel Metric for Image Segmentation | IEEE Journals & Magazine | IEEE Xplore

Fuzzy C-Means Clustering With Local Information and Kernel Metric for Image Segmentation

Publisher: IEEE

Abstract:

In this paper, we present an improved fuzzy C-means (FCM) algorithm for image segmentation by introducing a tradeoff weighted fuzzy factor and a kernel metric. The tradeo...View more

Abstract:

In this paper, we present an improved fuzzy C-means (FCM) algorithm for image segmentation by introducing a tradeoff weighted fuzzy factor and a kernel metric. The tradeoff weighted fuzzy factor depends on the space distance of all neighboring pixels and their gray-level difference simultaneously. By using this factor, the new algorithm can accurately estimate the damping extent of neighboring pixels. In order to further enhance its robustness to noise and outliers, we introduce a kernel distance measure to its objective function. The new algorithm adaptively determines the kernel parameter by using a fast bandwidth selection rule based on the distance variance of all data points in the collection. Furthermore, the tradeoff weighted fuzzy factor and the kernel distance measure are both parameter free. Experimental results on synthetic and real images show that the new algorithm is effective and efficient, and is relatively independent of this type of noise.
Published in: IEEE Transactions on Image Processing ( Volume: 22, Issue: 2, February 2013)
Page(s): 573 - 584
Date of Publication: 18 September 2012

ISSN Information:

PubMed ID: 23008257
Publisher: IEEE

References

References is not available for this document.