Application of Rough Set and K-means clustering in image segmentation

Yan Liu; Ying Juan Yue; Yan Jun Li; Ke Zhang

Application of Rough Set and K-means clustering in image segmentation

Yan Liu, Ying Juan Yue, Yan Jun Li, Ke Zhang

航天学院

科研成果: 期刊稿件 › 文章 › 同行评审

6 引用（Scopus）

摘要

Rough Set theory is a new mathematical tool to deal with problems on vagueness and uncertainty. An image segmentation method based on Rough Set theory and K-means clustering is presented. The original image is segmented according to the relation of equal value. By applying value reduct to the attribute values, different regions are classified based on indiscernibility. The experimental results indicate that the method can improve veracity and stability of image segmentation.

源语言	英语
文章编号	1007-2276(2004)03-0300-03
页（从-至）	300-302
页数	3
期刊	Hongwai yu Jiguang Gongcheng/Infrared and Laser Engineering
卷	33
期	3
出版状态	已出版 - 6月 2004

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引用此

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keywords = "Image segmentation, K-means clustering, Rough Set",

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AU - Li, Yan Jun

AU - Zhang, Ke

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AB - Rough Set theory is a new mathematical tool to deal with problems on vagueness and uncertainty. An image segmentation method based on Rough Set theory and K-means clustering is presented. The original image is segmented according to the relation of equal value. By applying value reduct to the attribute values, different regions are classified based on indiscernibility. The experimental results indicate that the method can improve veracity and stability of image segmentation.

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KW - K-means clustering

KW - Rough Set

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M3 - 文章

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Application of Rough Set and K-means clustering in image segmentation

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