Image Segmentation Based on the Fuzzy Clustering Algorithm using Average Intracluster Distance


The Transactions of the Korea Information Processing Society (1994 ~ 2000), Vol. 7, No. 9, pp. 3029-3036, Sep. 2000
10.3745/KIPSTE.2000.7.9.3029,   PDF Download:

Abstract

Image segmentation is one of the important processes in the image information extraction for computer vision systems. The fuzzy clustering methods have been extensively used in the image segmentation because it extracts feature information of the region. Most of fuzzy clustering methods have used the Fuzzy C-means(FCM) algorithm. This algorithm can be misclassified about the different size of cluster because the degree of membership depends on highly the distance between data and the centroids of the clusters. This paper proposes a fuzzy clustering algorithm using the Average Intracluster Distance that classifies data uniformly without regard to the size of data sets. The Average Intracluster Distance takes an average of the vector set belong to each cluster and increases in exact proportion to its size and density. The experimental results demonstrate that the proposed approach has the good results by classification entropy and validity function.


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Cite this article
[IEEE Style]
H. J. You, K. S. Ahn, S. J. Cho, "Image Segmentation Based on the Fuzzy Clustering Algorithm using Average Intracluster Distance," The Transactions of the Korea Information Processing Society (1994 ~ 2000), vol. 7, no. 9, pp. 3029-3036, 2000. DOI: 10.3745/KIPSTE.2000.7.9.3029.

[ACM Style]
Hyun Jai You, Kang Sik Ahn, and Seok Je Cho. 2000. Image Segmentation Based on the Fuzzy Clustering Algorithm using Average Intracluster Distance. The Transactions of the Korea Information Processing Society (1994 ~ 2000), 7, 9, (2000), 3029-3036. DOI: 10.3745/KIPSTE.2000.7.9.3029.