A Multilevel Thresholding Approach Based on Levy-Flight Firefly Algorithm

Multilevel thresholding is an important technique for image processing. Many thresholding techniques have been proposed in the literature. Among them, the maximum entropy thresholding (MET) has been widely applied. In this paper is presented a novel optimal multilevel thresholding algorithm based on maximum entropy measure and L´evy flight Firefly algorithm (LFA) for image segmentation. This new method called, the maximum entropy based on L´evy flight Firefly algorithm for multilevel thresholding (MELFAMT) method. The proposed segmentation method is employed for five benchmark images and the performances obtained outperform results obtained with well-known methods, like Gaussian smoothing method, Symmetry-duality method, improved GA-based algorithm, the hybrid cooperative-comprehensive learning based PSO algorithm (HCOCLPSO)and A new social and momentum component adaptive PSO algorithm (SMCAPSO) for image segmentation.