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

School of Astronautics

Research output: Contribution to journal › Article › peer-review

6 Scopus citations

Abstract

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.

Original language	English
Article number	1007-2276(2004)03-0300-03
Pages (from-to)	300-302
Number of pages	3
Journal	Hongwai yu Jiguang Gongcheng/Infrared and Laser Engineering
Volume	33
Issue number	3
State	Published - Jun 2004

Keywords

Image segmentation
K-means clustering
Rough Set

Cite this

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title = "Application of Rough Set and K-means clustering in image segmentation",

abstract = "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.",

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

author = "Yan Liu and Yue, {Ying Juan} and Li, {Yan Jun} and Ke Zhang",

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AU - Liu, Yan

AU - Yue, Ying Juan

AU - Li, Yan Jun

AU - Zhang, Ke

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N2 - 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.

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.

KW - Image segmentation

KW - K-means clustering

KW - Rough Set

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M3 - 文章

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SN - 1007-2276

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JO - Hongwai yu Jiguang Gongcheng/Infrared and Laser Engineering

JF - Hongwai yu Jiguang Gongcheng/Infrared and Laser Engineering

IS - 3

M1 - 1007-2276(2004)03-0300-03

ER -

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

Abstract

Keywords

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