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Proceedings Paper

Analysis of image thresholding segmentation algorithms based on swarm intelligence
Author(s): Yi Zhang; Kai Lu; Yinghui Gao; Bo Yang
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Paper Abstract

Swarm intelligence-based image thresholding segmentation algorithms are playing an important role in the research field of image segmentation. In this paper, we briefly introduce the theories of four existing image segmentation algorithms based on swarm intelligence including fish swarm algorithm, artificial bee colony, bacteria foraging algorithm and particle swarm optimization. Then some image benchmarks are tested in order to show the differences of the segmentation accuracy, time consumption, convergence and robustness for Salt&Pepper noise and Gaussian noise of these four algorithms. Through these comparisons, this paper gives qualitative analyses for the performance variance of the four algorithms. The conclusions in this paper would give a significant guide for the actual image segmentation.

Paper Details

Date Published: 13 March 2013
PDF: 8 pages
Proc. SPIE 8783, Fifth International Conference on Machine Vision (ICMV 2012): Computer Vision, Image Analysis and Processing, 878306 (13 March 2013); doi: 10.1117/12.2010732
Show Author Affiliations
Yi Zhang, National Univ. of Defense Technology (China)
Kai Lu, National Univ. of Defense Technology (China)
Yinghui Gao, National Univ. of Defense Technology (China)
Bo Yang, National Univ. of Defense Technology (China)


Published in SPIE Proceedings Vol. 8783:
Fifth International Conference on Machine Vision (ICMV 2012): Computer Vision, Image Analysis and Processing
Yulin Wang; Liansheng Tan; Jianhong Zhou, Editor(s)

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