[1]刘咏梅,代丽洁.基于空间位置约束的K均值图像分割[J].智能系统学报,2010,5(01):67-69.
 LIU Yong-mei,DAI Li-jie.An improved method of Kmeans image segmentation based on spatial position information[J].CAAI Transactions on Intelligent Systems,2010,5(01):67-69.
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第5卷
期数:
2010年01期
页码:
67-69
栏目:
出版日期:
2010-02-25

文章信息/Info

Title:
An improved method of Kmeans image segmentation based on spatial position information
文章编号:
1673-4785(2010)01-0067-03
作者:
刘咏梅代丽洁
哈尔滨工程大学 计算机科学与技术学院,黑龙江 哈尔滨 150001
Author(s):
LIU Yong-mei DAI Li-jie
School of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China
关键词:
K均值聚类图像分割空间位置信息
Keywords:
Kmeans clustering image segmentation spatial position information
分类号:
TP391
文献标志码:
A
摘要:
K均值聚类分割是一种有效的基于聚类的图像分割算法.传统的K均值聚类分割算法采用特征空间中的相似性测度来度量像素的归属类别.由于自然景物图像的复杂性,位置邻近且本应属于同一分割区域的像素点,由于它们视觉特征的差异性,导致其在特征空间中相距甚远而被分割为不同的区域.以投票的方法将像素的局部空间位置信息引入到K均值聚类分割算法中,达到了改善分割效果的目的.实验结果证实了该方法的有效性.
Abstract:
Kmeans clustering is an effective algorithm for image segmentation, which attempts to separate objects of interest from their background. Traditional Kmeans clustering algorithms use the visual similarity measures of pixels in the feature space to determine which segmentation region the pixels belong to. Because of the complexity of natural images, neighboring pixels with different visual features, which should be treated as part of the same object, may end up in separate regions. As a result, it is hard to get satisfactory results when depending only on visual features. A spatially constrained image segmentation algorithm was therefore developed. It improved on the Kmeans clustering algorithm by adding a corrective step, the application of positional information from neighboring pixels. Experiments showed that the algorithm is effective.

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备注/Memo

备注/Memo:
收稿日期:2008-09-01.
通信作者:刘咏梅.E-mail:liuyongmei@hrbeu.edu.cn.
作者简介:
刘咏梅,女,1973年生,副教授、硕士生导师、博士.主要研究方向为模式识别、图像处理、生物信息学. 
 代丽洁,女,1983年生,硕士研究生,主要研究方向为图像处理与模式识别、图像标注.
更新日期/Last Update: 2010-03-31