Comparison of evolutionary and swarm based computational techniques for multilevel color image thresholding

Kurban T., Civicioglu P., KURBAN R., BEŞDOK E.

APPLIED SOFT COMPUTING, vol.23, pp.128-143, 2014 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 23
  • Publication Date: 2014
  • Doi Number: 10.1016/j.asoc.2014.05.037
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.128-143
  • Keywords: Color image thresholding, Evolutionary optimization algorithms, Swarm-based optimization algorithms, DIFFERENTIAL EVOLUTION, PARTICLE SWARM, GLOBAL OPTIMIZATION, FUZZY ENTROPY, SELECTION, SEGMENTATION, ALGORITHMS, QUANTIZATION, RECOGNITION, COMPRESSION
  • Gazi University Affiliated: No


This paper introduces the comparison of evolutionary and swarm-based optimization algorithms for multilevel color image thresholding problem which is a process used for segmentation of an image into different regions. Thresholding has various applications such as video image compression, geovideo and document processing, particle counting, and object recognition. Evolutionary and swarm-based computation techniques are widely used to reduce the computational complexity of the multilevel thresholding problem. In this study, well-known evolutionary algorithms such as Evolution Strategy, Genetic Algorithm, Differential Evolution, Adaptive Differential Evolution and swarm-based algorithms such as Particle Swarm Optimization, Artificial Bee Colony, Cuckoo Search and Differential Search Algorithm have been used for solving multilevel thresholding problem. Kapur's entropy is used as the fitness function to be maximized. Experiments are conducted on 20 different test images to compare the algorithms in terms of quality, running CPU times and compression ratios. According to the statistical analysis of objective values, swarm based algorithms are more accurate and robust than evolutionary algorithms in general. However, experimental results exposed that evolutionary algorithms are faster than swarm based algorithms in terms of CPU running times. (C) 2014 Elsevier B.V. All rights reserved.