Image Segmentation Using Artificial Bee Colony Optimization

  • Erik Cuevas
  • Felipe Sención-Echauri
  • Daniel Zaldivar
  • Marco Pérez
Part of the Intelligent Systems Reference Library book series (ISRL, volume 38)

Abstract

This chapter explores the use of the Artificial Bee Colony (ABC) algorithm to compute pixel classification for image segmentation. ABC is a heuristic algorithm motivated by the intelligent behaviour of honey-bees which has been successfully employed to solve complex optimization problems. In this approach, an image 1-D histogram is approximated through a Gaussian mixture model whose parameters are calculated by the ABC algorithm. For the approximation scheme, each Gaussian function represents a pixel class and therefore a threshold. Unlike the Expectation-Maximization (EM) algorithm, the ABC-based method shows fast convergence and low sensitivity to initial conditions. Remarkably, it also improves complex time-consuming computations commonly required by gradient-based methods. Experimental results demonstrate the algorithm’s ability to perform automatic multi-threshold selection yet showing interesting advantages by comparison to other well-known algorithms.

Keywords

Image Segmentation Expectation Maximization Gaussian Mixture Model Swarm Intelligence Expectation Maximization Algorithm 
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

  • Erik Cuevas
    • 1
  • Felipe Sención-Echauri
    • 1
  • Daniel Zaldivar
    • 1
  • Marco Pérez
    • 1
  1. 1.Departamento de Ciencias ComputacionalesUniversidad de Guadalajara, CUCEIGuadalajaraMéxico

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