Volume 13 Number 1 (Jan. 2018)
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JCP 2018 Vol.13(1): 130-138 ISSN: 1796-203X
doi: 10.17706/jcp.13.1.130-138

A New Image Segmentation Algorithm Based on Particle Swarm Optimization and Rough Set

Wafaa Ghonaim1, Shereen A. Taie2
1Faculty of Science, Al-Azhar University, Cairo, Egypt.
2Computer Science Department, Faculty of Computers and Information, Fayoum University, Egypt.

Abstract—Image segmentation is a basic technique for advanced image analysis. In this paper a new image segmentation algorithm based on combining particle swarm optimization PSO and rough set is proposed. The algorithm adopts mean roughness measure as evaluation standard, this measure depends on minimization of roughness in both object and background regions; by determining the optimal threshold of partitioning. In this algorithm, threshold estimation is regarded as a search procedure that searches for an optimal value in a continuous gray-scale interval. The results of the PSO based proposed algorithm are compared with Bat-Inspired algorithm under mean roughness measure as the fitness function and simulations show that the PSO seems much superior than Bat algorithm.

Index Terms—Particle swarm optimization, bat-inspired algorithm, rough set, image segmentation, mean roughness measure.

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Cite:Wafaa Ghonaim, Shereen A. Taie, "A New Image Segmentation Algorithm Based on Particle Swarm Optimization and Rough Set," Journal of Computers vol. 13, no. 1, pp. 130-138 , 2018.

General Information

ISSN: 1796-203X
Frequency: Monthly (2006-2014); Bimonthly (Since 2015)
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Cherry L. Chen
Abstracting/ Indexing: DBLP, EBSCO, DOAJ, ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, CNKI,etc
E-mail: jcp@iap.org
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