Fast Multilevel Thresholding Method for Image Segmentation Based on Improved Particle Swarm Optimization and Maximal Variance

Zheng Tao Peng; Kang Ling Fang; Zhi Qi Su; Shi Hong Li

doi:10.4028/www.scientific.net/AMR.532-533.1741

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 532-533Fast Multilevel Thresholding Method for Image...

Fast Multilevel Thresholding Method for Image Segmentation Based on Improved Particle Swarm Optimization and Maximal Variance

Abstract:

To determine the optimal thresholds in image segmentation, a new multilevel thresholding method based on improved particle swarm optimization (IPSO) is proposed in this paper. Firstly, use the conception of independent peaks to divide the histogram to several regions, secondly, the optimization object function using maximum between-class variance (MV) method can be gotten in each area, by the non-uniform mutation and Geese-LDW PSO optimization of the object function, the optimal thresholds can be gotten, and the image can be segmented with the thresholds. Compared with the basic MV algorithm and genetic algorithm (GA) modified MV, the experimental results show that the new method not only realizes the image segmentation well, but also improves the speed.

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Periodical:

Advanced Materials Research (Volumes 532-533)

Pages:

1741-1746

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.532-533.1741

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Online since:

June 2012

Authors:

Zheng Tao Peng, Kang Ling Fang, Zhi Qi Su, Shi Hong Li

Keywords:

Image Segmentation, Independent Peaks, Linear Descend Inertia Weight, Maximal Variance, Multilevel Threshold, Non-Uniform Mutation, Particle Swarm Optimization (PSO)

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References

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