An improved K-means clustering algorithm in agricultural image segmentation

Huifeng Cheng; Hui Peng; Shanmei Liu

doi:10.1117/12.2020131

4 March 2013 An improved K-means clustering algorithm in agricultural image segmentation

Huifeng Cheng, Hui Peng, Shanmei Liu

Proceedings Volume 8761, PIAGENG 2013: Image Processing and Photonics for Agricultural Engineering; 87610G (2013) https://doi.org/10.1117/12.2020131
Event: Third International Conference on Photonics and Image in Agriculture Engineering (PIAGENG 2013), 2013, Sanya, China

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.

Citation Download Citation

Huifeng Cheng, Hui Peng, and Shanmei Liu "An improved K-means clustering algorithm in agricultural image segmentation", Proc. SPIE 8761, PIAGENG 2013: Image Processing and Photonics for Agricultural Engineering, 87610G (4 March 2013); https://doi.org/10.1117/12.2020131

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