Abstract
For its simplicity and applicability, fuzzy c-means clustering algorithm is widely used in image segmentation. However, fuzzy c-means clustering algorithm has some problems in image segmentation, such as sensitivity to noise, local convergence, etc. In order to overcome the fuzzy c-means clustering shortcomings, this paper replaces Euclidean distance with Mahalanobis distance in the fuzzy c-means clustering algorithm. Experimental results show that the proposed algorithm has a significant improvement on the effect and efficiency of segmentation comparing with the standard FCM clustering algorithm.
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Zhang, Y., Li, Z., Cai, J., Wang, J. (2010). Image Segmentation Based on FCM with Mahalanobis Distance. In: Zhu, R., Zhang, Y., Liu, B., Liu, C. (eds) Information Computing and Applications. ICICA 2010. Lecture Notes in Computer Science, vol 6377. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16167-4_27
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DOI: https://doi.org/10.1007/978-3-642-16167-4_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16166-7
Online ISBN: 978-3-642-16167-4
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