Improved support vector clustering algorithm for color image segmentation

Authors

  • Yongqing Wang Department of Computer Science and Applications, Zhengzhou Institute of Aeronautical Industry Management, Zhengzhou 450015, China
  • Xiling Liu College of Information & Business, Zhongyuan University of Technology, Zhengzhou 450007, China

Keywords:

Image processing, Color image segmentation, Support vector clustering, MEB algorithm, Massive data

Abstract

Color image segmentation has attracted more and more attention in various application fields during the past few years. Essentially speaking, color image segmentation problem is a process of clustering according to the color of pixels. But, traditional clustering methods do not scale well with the number of training sample, which limits the ability of handling massive data effectively. With the utilization of an improved approximate Minimum Enclosing Ball algorithm, this article develops an fast support vector clustering algorithm for computing the different clusters of given color images in kernel-introduced space to segment the color images. We prove theoretically that the proposed algorithm converges to the optimum within any given precision quickly. Compared to other popular algorithms, it has the competitive performances both on training time and accuracy. Color image segmentation experiments on both synthetic and real-world data sets demonstrate the validity of the proposed algorithm.

Author Biography

Yongqing Wang, Department of Computer Science and Applications, Zhengzhou Institute of Aeronautical Industry Management, Zhengzhou 450015, China

Yongqing Wang, received his B.S.degree in fundamental mathematics from Henan Normal University, China, in June 2000, his M.S. degree in control theory and control engineering from Henan Normal University, China, in June 2003, and his Ph.D. degree in computer science at the Institute of Automation of the Chinese Academy of Sciences, in April 2009. He is a lecturer at the Department of Computer Science and Applications, Zhengzhou Institute of Aeronautical Industry Management, China. His current research interest includes machine learning and data mining.

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Published

2015-05-27

Issue

Engineering Review, Vol. 35, No. 2

Section

Articles

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