关键词: 模糊C均值聚类, 抗噪性, 道岔缺口, 图像分割

Abstract:

Fuzzy cmeans (FCM) clustering has an excessive dependence on initial parameters. The quantum particle swarm is adopted to optimize initial clustering center of FCM to address its sensitivity to the initial clustering center. The particle swarm optimization algorithm has stronger global searching ability, but insufficient local search ability. The quantum rotating gate changes the movement of the particles and increases the diversity of population, thus better local optimization ability of particle swarm optimization algorithm. The quantum particle swarm optimization algorithm is used to search the initial clustering center of FCM algorithm. The experimental simulation shows that the improved algorithm can speed up the search while obtaining more stable clustering center and better segmentation effect than the standard FCM algorithm segmentation effect.

Key words: FCM, noise immunity, rail gap, image segmentation