Multilevel Thresholding Using Chaos Optimization and Differential Evolution Algorithm

Multilevel thresholding usually is much computationally exhaustive in the process of searching the optimal thresholds. In order to improve computational efficiency, this paper presents an image segmentation method by using the chaos optimization algorithm (COA), which is incorporated into differential evolution (DE). The stochastic property and space ergodicity of chaos mapping are utilized to enlarge the search range and to explore a huge search space. Additionally, to find the optimal thresholds, the differential evolution with chaos optimization algorithm (DECOA) is considered by using the objective model based on the maximum entropy criterion. The presented segmentation method has been simulated on six standard test images and compared with the canonical DE and some other classic optimization algorithms. Experimental results show that the presented DECOA algorithm has much faster convergence speed than those of some existing methods. Furthermore, this algorithm can get superior segmentation performance of the feasibility and effectiveness.