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Image Segmentation with Improved Artificial Fish Swarm Algorithm

  • Mingyan Jiang
  • Nikos E. Mastorakis
  • Dongfeng Yuan
  • Miguel Angel Lagunas
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 28)

Abstract

Some improved adaptive methods about step length are proposed in the Artificial Fish Swarm Algorithm (AFSA), which is a new heuristic intelligent optimization algorithm. The experimental results show that proposed methods have better performances such as good and fast global convergence, strong robustness, insensitivity to initial values, and simplicity of implementation. We apply the method in the image processing for the multi-threshold image segmentation compared with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The properties are discussed and analysed at the end.

Keywords

Genetic Algorithm Particle Swarm Optimization Image Segmentation Particle Swarm Optimization Method Artificial Fish 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This work is supported by the National Natural Scientific Foundation of China (No. 60672036, No. 60672037), the Natural Science Foundation of Shandong Province of China (No. Y2006G06), and the Catalan Government (Generalitat de Catalunya, Spain) under grant SGR2005-00690.

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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Mingyan Jiang
    • 1
  • Nikos E. Mastorakis
    • 2
  • Dongfeng Yuan
    • 3
  • Miguel Angel Lagunas
    • 1
  1. 1.Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), Av. Del, Canal Olímpic s/n, 08860 Castelldefels (Barcelona)Spain School of Information Science and Engineering, Shandong UniversityChina
  2. 2.Military Insitutes of University Education (ASEI) Hellenic Naval AcademyTerma ChatzikyriakouGreece
  3. 3.School of Information Science and EngineeringShandong UniversityChina

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