Two-Dimensional Maximum Entropy Infrared Image Fast Segmentation Based on Chicken Swarm Optimization
Ben Niu, Xiaodong Mu, Zhaoxiang Yi, Pei Hu · 2018
Aiming at the problem of large computing and time-consuming in two-dimensional maximum entropy based image segmentation method, this paper proposes a two-dimensional maximum entropy segmentation algorithm based on chicken swarm optimization. Chicken swarm optimization algorithm has certain advantage in convergence speed and convergence accuracy. Using these advantages, the objective function of two-dimensional maximum entropy is considered as the fitness function of chicken swarm optimization algorithm. And this method located the best threshold gradually and quickly by virtue of the role division of chickens and teamwork of roosters, hens and chicks. Experimental results show that the method is superior to some segmentation methods based on particle swarm optimization algorithm and artificial fish swarm algorithm in convergence and segmentation effects.