关键词: 图像分割, Otsu,粒子群优化算法,惯性权重,适应值

Abstract: The Otsu image segmentation algorithm has good adaptability due to its contents-independent characteristics．However,its shortcomings like large amount of computation and poor real time quality have limited its application．To solve this problem,we proposed a new segmentation algorithm using the principle of Otsu based on an improved PSO algorithm．Taking the class-between variance of Otsu as the fitness function of PSO,the current segmentation threshold as the particle’s current location,and the updating speed of threshold as the particle's current speed,and using the improvement of particle’s best fitness value as the inertia weight of PSO,the proposed algorithm searches for the thre-shold which makes the maximum value of the class-between variance in grey space dynamically．The experimental results show that the new algorithm can get segmentation result which is equal to the classic Otsu,significantly reduces the time of segmentation process and also has higher efficiency.

Key words: Image segmentation,Otsu,Particle swarm optimization,Inertia weight,Fitness value