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Mathematical Problems in Engineering
Volume 2014, Article ID 464875, 13 pages
Research Article

KmsGC: An Unsupervised Color Image Segmentation Algorithm Based on -Means Clustering and Graph Cut

1College of Computer Science, Sichuan University, Chengdu, Sichuan 610065, China
2College of Mathematics and Information Science, Guangxi University, Nanning, Guangxi 530004, China

Received 26 August 2013; Revised 17 March 2014; Accepted 2 April 2014; Published 12 May 2014

Academic Editor: Gerhard-Wilhelm Weber

Copyright © 2014 Binmei Liang and Jianzhou Zhang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


For unsupervised color image segmentation, we propose a two-stage algorithm, KmsGC, that combines -means clustering with graph cut. In the first stage, -means clustering algorithm is applied to make an initial clustering, and the optimal number of clusters is automatically determined by a compactness criterion that is established to find clustering with maximum intercluster distance and minimum intracluster variance. In the second stage, a multiple terminal vertices weighted graph is constructed based on an energy function, and the image is segmented according to a minimum cost multiway cut. A large number of performance evaluations are carried out, and the experimental results indicate the proposed approach is effective compared to other existing image segmentation algorithms on the Berkeley image database.