Fuzzy Particle Swarm Clustering of Infrared Images

作者：Yongfeng Xu, Zhang Shu-ling · 年份：2009 · DOI：10.1109/icic.2009.139 · 被引用次数：17 · 研究领域：Infrared Target Detection Methodologies、Advanced Measurement and Detection Methods、Video Surveillance and Tracking Methods

Considering the characteristics of the inconspicuous difference between targets and backgrounds and the low contrast in infrared images, an adaptive clustering algorithm based on fuzzy particle swarm optimization is used in the infrared image processing. Fuzzy C-mean (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, particle swarm optimization (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 clustering criterion function of FCM as the object function of PSO, a new hybrid image clustering algorithm based on particle swarm optimization and fuzzy C-mean algorithm is proposed. Experiments show that the new algorithm can get the optimal threshold by the maximum entropy.