A Color Image Segmentation Method Based on Improved K-Means Clustering Algorithm

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 217)

Abstract

How to effectively segment objects in the color images is the key point in the computer vision and image analysis. All kinds of segment algorithms have been proposed by many scholars, which could be basically divided into three categories. This paper presents repeated usage of the optimal threshold for roughly extracting the largest target area of the color image. Then an improved K-means clustering algorithm is used to improve the accuracy of the segmentation from the target area. Experimental results show that this method can effectively extract color image from an object. It has also a certain degree of robustness to the noisy image.

Keywords

Image segmentation Optimal threshold K-mean clustering algorithm Robustness 

Notes

Acknowledgments

This work was supported by Science and Technology Research Program of Zhejiang Province under grant 2011C21036, Shanghai Natural Science Foundation under grant 10ZR1400100 and the new-shoot Talents Program of Zhejiang Province.

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Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.Zhejiang Wanli UniversityNingboChina
  2. 2.Donghua UniversityShanghaiChina

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