Threshold image segmentation based on granular immune algorithm

  • Authors:

  • Xu Xinying;Zhang Zhijun;Xie Jun;Xie Keming

  • Affiliations:

  • College of Information Engineering, Taiyuan University of Technology, Taiyuan, China;College of Information Engineering, Taiyuan University of Technology, Taiyuan, China and College of Information Management, Shanxi University of Finance and Economics, Taiyuan, China;College of Information Engineering, Taiyuan University of Technology, Taiyuan, China;College of Information Engineering, Taiyuan University of Technology, Taiyuan, China

  • Venue:
  • CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
  • Year:
  • 2009

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Abstract

Image segmentation is an important processing step in many image, video and computer vision applications. Artificial Immune Systems (AIS) is a diverse area of research that attempts to bridge the divide between immunological and engineering. In this paper, we present a threshold method based on granular immune algorithm (GIA) for image segmentation, which includes granular hierarchy and immunological mechanism. Based on two granular hierarchies, the method can not only execute multi-point parallel search from local to global searching field but also find better solutions with small generation and mean numbers of function values. So this method has better performance in stabilization and convergence that GA. Our experimental results indicate that the proposed method here is very suitable for image segmentation