Multilevel Image Thresholding Selection Based on the Firefly Algorithm

Abstract

The multilevel thresholding is an important technique for image processing and pattern recognition. The maximum entropy thresholding has been widely applied in the literature. In this paper, a new multilevel MET algorithm based on the technology of the firefly algorithm is proposed. This proposed method is called the maximum entropy based firefly thresholding method. Four different methods are implemented for comparing to this proposed method: the exhaustive search, the particle swarm optimization, the hybrid cooperative-comprehensive learning based PSO algorithm and the honey bee mating optimization. The experimental results demonstrated that the proposed MEFFT algorithm can search for multiple thresholds which are very close to the optimal ones examined by the exhaustive search method. Compared to the PSO and HCOCLPSO, the segmentation results of using the MEFFT algorithm is significantly improved and the computation time of the proposed MEFFT algorithm is shortest.