Abstract
Image segmentation is an important research topic in the field of computer vision. Now the fuzzy C-Means (FCM) algorithm is one of the most frequently used clustering algorithms. Although a FCM algorithm is a clustering without supervising, the FCM arithmetic should be given the transcendent information of prototype parameter; otherwise the arithmetic will be wrong. This limits its application in image segmentation. In this paper, we develop a new theoretical approach to automatically selecting the weighting exponent in the FCM to segment the image, which is called Automatic Clustering Weighting Fuzzy C-Means Segmentation (ACWFCM). This method can reduce the disturbance of noise; get the segmentation numbers more accurately. The experimental results illustrate the effectiveness of the proposed method.
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© 2012 Springer-Verlag GmbH Berlin Heidelberg
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Li, Y. et al. (2012). An Automatic Image Segmentation Algorithm Based on Weighting Fuzzy C-Means Clustering. In: Luo, J. (eds) Soft Computing in Information Communication Technology. Advances in Intelligent and Soft Computing, vol 158. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29148-7_5
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DOI: https://doi.org/10.1007/978-3-642-29148-7_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29147-0
Online ISBN: 978-3-642-29148-7
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