A Multilevel Thresholding Approach Based on L´evy-Flight Firefly Algorithm for Image Segmentation

Multilevel thresholding is an important technique for image processing. The maximum entropy thresholding (MET) has been widely applied in the literature. This paper presented a novel optimal multilevel thresholding approach based on the maximum entropy measure and L´evy-Flight Firefly Algorithm (LFA) for image segmentation. This new method was called, the maximum entropy based on l´evy-flight firefly algorithm for multilevel thresholding (MELFAT) method. In this paper, five famous benchmark images were used to evaluate the proposed method and the results were evaluated by the uniformity measure. The obtained results were compared with five wellknown methods, like Gaussians mooting method (Lim, Y. K., & Lee, S. U. (۱۹۹۰), Symmetry-duality method (Yin, P. Y., & Chen, L. H. (۱۹۹۳), improved GA-based algorithm (Yin, P. -Y. (۱۹۹۹), the hybrid cooperative-comprehensive learning based PSO algorithm (HCOCLPSO) ( Maitra, M., & Chatterjee, A. (۲۰۰۸)) and a new social and momentum component adaptive PSO algorithm (SMCAPSO) (Chander, A.,& Chatterjee, A.,& Siarry, P.(۲۰۱۱)) . The experimental results confirmed the performance and capability of the proposed method to find optimal threshold values.

Keywords:

multilevel thresholding , entropy , image segmentation , uniformity , levy-flight firefly algorithm