Multi-level Thresholding Selection by using the Honey Bee Mating Optimization

Image thresholding is an important technique for image processing and pattern recognition. In this paper, a new multilevel image thresholding algorithm based on the technology of the honey bee mating optimization (HBMO) is proposed. Three different methods such as the particle swarm optimization (PSO), the hybrid cooperative-comprehensive learning based PSO algorithm (HCOCLPSO) and the Fast Otsu's method are also implemented for comparison with the results of the proposed method. The experimental results reveal two important interested results for other three image thresholding methods. One is that the results of PSO and Fast Ostu's method are unstable that extraordinary segmentations are generated. Another is that the results of HCOCLPSO are superior to original PSO method, but it still slower than ones of HBMO and it had similar segmentation results with the ones of the honey bee mating optimization.