Hybrid BBO-DE Algorithms for Fuzzy Entropy-Based Thresholding

  • Ilhem Boussaïd
  • Amitava Chatterjee
  • Patrick Siarry
  • Mohamed Ahmed-Nacer
Chapter

Abstract

This chapter shows how a recently proposed stochastic optimization algorithm, called biogeography-based optimization (BBO), can be efficiently employed for development of three-level thresholding-based image segmentation. This technique is utilized to determine suitable thresholds utilizing a fuzzy entropy-based fitness function, which the optimization procedure attempts to maximize. The chapter demonstrates how improved BBO-based strategies, employing hybridizations with differential evolution (DE) algorithms, can be employed to incorporate diversity in the basic BBO algorithm that can help the optimization algorithm avoid getting trapped at local optima and seek the global optimum in a more efficient manner. Several such hybrid BBO-DE algorithms have been utilized for this optimum thresholding-based image segmentation procedure. A detailed implementation analysis for a popular set of well-known benchmark images has been carried out to qualitatively and quantitatively demonstrate the utility of the proposed hybrid BBO-DE optimization algorithm.

Keywords

Membership Function Gray Level Differential Evolution Differential Evolution Algorithm Mutation Scheme 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ilhem Boussaïd
    • 1
  • Amitava Chatterjee
    • 2
  • Patrick Siarry
    • 3
  • Mohamed Ahmed-Nacer
    • 1
  1. 1.University of Science and Technology Houari Boumediene (USTHB)Bab-EzzouarAlgeria
  2. 2.Electrical Engineering DepartmentJadavpur UniversityKolkataIndia
  3. 3.Université de Paris-Est Créteil Val de MarneCréteilFrance

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