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

被引：19

作者：

Yusoff, Intan Aidha ^{[1
]}

Isa, Nor Ashidi Mat ^{[1
]}

Hasikin, Khairunnisa ^{[1
]}

机构：

[1] Univ Sains Malaysia, Imaging & Intelligent Syst Res Team ISRT, Sch Elect & Elect Engn, Nibong Tebal 14300, Pulau Pinang, Malaysia

来源：

COMPUTERS & ELECTRICAL ENGINEERING | 2013年 / 39卷 / 03期

关键词：

ENHANCEMENT;

D O I：

10.1016/j.compeleceng.2012.11.013

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

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. (C) 2012 Elsevier Ltd. All rights reserved.

引用

页码：907 / 917

页数：11

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