Automated two-dimensional K-means clustering algorithm for unsupervised image segmentation

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• 作者：Intan Aidha Yusoff ^{intan_aidha@yahoo.com}Author Vitae ; Nor Ashidi Mat Isa ; ^{ashidi@eng.usm.my}Author Vitae ; Khairunnisa Hasikin ^{eishahasikin@gmail.com}Author Vitae
• 刊名：Computers and Electrical Engineering
• 出版年：2013
• 出版时间：April, 2013
• 年：2013
• 卷：39
• 期：3
• 页码：907-917
• 全文大小：2674 K

文摘

This paper introduces the Automated Two-Dimensional K-Means (A2DKM) algorithm, a novel unsupervised clustering technique. The proposed technique differs from the conventional clustering techniques because it eliminates the need for users to determine the number of clusters. In addition, A2DKM incorporates local and spatial information of the data into the clustering analysis. A2DKM is qualitatively and quantitatively compared with the conventional clustering algorithms, namely, the K-Means (KM), Fuzzy C-Means (FCM), Moving K-Means (MKM), and Adaptive Fuzzy K-Means (AFKM) algorithms. The A2DKM outperforms these algorithms by producing more homogeneous segmentation results.