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Proceedings Paper

An improved K-means clustering algorithm in agricultural image segmentation
Author(s): Huifeng Cheng; Hui Peng; Shanmei Liu
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Paper Abstract

Image segmentation is the first important step to image analysis and image processing. In this paper, according to color crops image characteristics, we firstly transform the color space of image from RGB to HIS, and then select proper initial clustering center and cluster number in application of mean-variance approach and rough set theory followed by clustering calculation in such a way as to automatically segment color component rapidly and extract target objects from background accurately, which provides a reliable basis for identification, analysis, follow-up calculation and process of crops images. Experimental results demonstrate that improved k-means clustering algorithm is able to reduce the computation amounts and enhance precision and accuracy of clustering.

Paper Details

Date Published: 4 March 2013
PDF: 5 pages
Proc. SPIE 8761, PIAGENG 2013: Image Processing and Photonics for Agricultural Engineering, 87610G (4 March 2013); doi: 10.1117/12.2020131
Show Author Affiliations
Huifeng Cheng, Chongqing Technology and Business Univ. (China)
Hui Peng, Huazhong Agricultural Univ. (China)
Shanmei Liu, Huazhong Agricultural Univ. (China)


Published in SPIE Proceedings Vol. 8761:
PIAGENG 2013: Image Processing and Photonics for Agricultural Engineering
Honghua Tan, Editor(s)

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