Multilevel minimum cross entropy threshold selection based on the honey bee mating optimization

Image thresholding is an important technique for image processing and pattern recognition. Many thresholding techniques have been proposed in the literature. Among them, the minimum cross entropy thresholding (MCET) has been widely applied. In this paper, a new multilevel MCET algorithm based on the technology of the honey bee mating optimization (HBMO) is proposed. Three different methods included the exhaustive search, the particle swarm optimization (PSO) and the quantum particle swarm optimization (QPSO) methods are also implemented for comparison with the results of the proposed method. The experimental results manifest that the proposed HBMO-based MCET algorithm can efficiently search for multiple thresholds which are very close to the optimal ones examined by the exhaustive search method. In comparison with the other two thresholding methods, the segmentation results using the HBMO-based MCET algorithm is the best. Furthermore, the convergence of the HBMO-based MCET algorithm can rapidly achieve, and the results are validated that the proposed HBMO-based MCET algorithm is efficient.