Cuckoo Search and Firefly Algorithm Applied to Multilevel Image Thresholding

  • Ivona BrajevicEmail author
  • Milan Tuba
Part of the Studies in Computational Intelligence book series (SCI, volume 516)


Multilevel image thresholding is a technique widely used in image processing, most often for segmentation. Exhaustive search is computationally prohibitively expensive since the number of possible thresholds to be examined grows exponentially with the number of desirable thresholds. Swarm intelligence metaheuristics have been used successfully for such hard optimization problems. In this chapter we investigate performance of two relatively new swarm intelligence algorithms, cuckoo search and firefly algorithm, applied to multilevel image thresholding. Particle swarm optimization and differential evolution algorithms have also been implemented for comparison. Two different objective functions, Kapur’s maximum entropy thresholding function and multi Otsu between-class variance, were used on standard benchmark images with known optima from exhaustive search (up to five threshold points). Results show that both, cuckoo search and firefly algorithm, exhibit superior performance and robustness.


Swarm intelligence Nature inspired algorithms Optimization metaheuristics Cuckoo search Firefly algorithm Image processing Multilevel image thresholding 



This reserach was supported by Ministry of Education and Science of Republic of Serbia, Grant III-44006.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.University of BelgradeBelgradeSerbia
  2. 2.Megatrend University BelgradeN. BelgradeSerbia

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