Computer Engineering and Applications ›› 2011, Vol. 47 ›› Issue (10): 169-170.

• 图形、图像、模式识别 • Previous Articles     Next Articles

Fuzzy particle swarm optimization algorithm for color quantization

XU Yongfeng,ZHANG Shuling   

  1. Department of Mathematic,Northwest University,Xi’an 710069,China
  • Received:1900-01-01 Revised:1900-01-01 Online:2011-04-01 Published:2011-04-01



  1. 西北大学 数学系,西安 710069

Abstract: A new algorithm for color quantization based on Fuzzy C-Mean(FCM) and Particle Swarm Optimization(PSO) algorithm is proposed.FCM clustering algorithm is a local search algorithm because it is easily trapped local optimum and is sensitive to initial value effectively.On the other hand,PSO algorithm is a global optimization algorithm.By incorporating the local search ability of FCM algorithm and the global optimization ability of PSO,and taking the criterion function of FCM as the object function of PSO,a new hybrid color quantization algorithm based on PSO and FCM algorithm is proposed.Experiments show that the new algorithm can get the optimal quantization image by PSNR and RMSE.

Key words: color quantization, Fuzzy C-Mean(FCM), Particle Swarm Optimization(PSO)

摘要: 把粒子群算法应用到色彩量化中,结合已有的模糊C均值聚类量化方法,提出了一种基于粒子群优化的色彩量化算法。模糊C均值聚类量化算法是一种局部搜索算法,对初始值较为敏感,容易陷入局部极小值而不能得到全局最优解;PSO算法是一种基于群体的具有全局寻优能力的优化方法。将模糊C均值聚类量化算法和PSO算法结合起来,把模糊C均值聚类量化算法的聚类准则函数作为PSO算法中的粒子适应度函数。仿真实验表明,新算法在均方根误差和峰值信噪比评判准则下能够得到最优的量化结果。

关键词: 色彩量化, 模糊C均值, 粒子群优化