Abstract
Fuzzy c-means (FCM) clustering algorithm as an unsupervised fuzzy clustering technique has been widely used in image segmentation. However, the conventional FCM algorithm is very sensitive to noise for the reason of incorporating no information about spatial context while segmentation. To overcome this limitation of FCM algorithm, a novel penalized fuzzy c-means (PFCM) algorithm for image segmentation is presented in this paper. The algorithm is realized by incorporating the spatial neighborhood information into the original FCM algorithm by a penalty term, which is employed to take into account the spatial dependence of the objects. Experiments demonstrate the proposed algorithm is effective and more robust to noise and other artifacts than the standard FCM algorithm.
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Yang, Y., Zhang, F., Zheng, C., Lin, P. (2005). Unsupervised Image Segmentation Using Penalized Fuzzy Clustering Algorithm. In: Gallagher, M., Hogan, J.P., Maire, F. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2005. IDEAL 2005. Lecture Notes in Computer Science, vol 3578. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11508069_10
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DOI: https://doi.org/10.1007/11508069_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26972-4
Online ISBN: 978-3-540-31693-0
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