Maximum 2D Entropy Image Segmentation Based on Artificial Bee Colony Optimization
Aiming at the problem of large computing in maximum 2D entropy based image segmentation method,this paper proposes a maximum 2D entropy image segmentation algorithm based on artificial bee colony optimization.Artificial bee colony algorithm has certain advantage in convergence speed,prevents local optimization,and has few control parameters.Using these advantages,the best 2D threshold of maximum 2D entropy method is considered as nectar,and artificial bee colony optimized maximum 2D entropy method is used to segment images.Experimental result shows that,compared with other methods,constriction of this method is quicker,stability is better and resistance to the noise is stronger.