MULTI-THRESHOLD INFRARED IMAGE SEGMENTATION BASED ON THE MODIFIED PARTICLE SWARM OPTIMIZATION ALGORITHM

摘要：

Threshold extraction is the fundamental step in multi-threshold image segmentation.This paper has introduced particle swarm optimization algorithm (PSO) for threshold extraction.But when dealing with the peaky high dimension function of maximum entropy for multi-threshold image segmentation, the conventional PSO is apt to be trapped in local optima called premature.This can cause image segmentation failure.This paper proposes a modified particle swarm optimization method (MPSO), which improves convergence speed and search capacity and avoid the premature phenomena when used in threshold extraction.Simulation results show that the MPSO has better performance and quicker speed.The experimental results also show that with the modified PSO as a threshold extraction method, the image is segmented fairly well and the segmentation speed improves greatly.

关键词： Particle swarm optimization algorithm Multi-threshold Infrared image segmentation

作者: YI-TONG LIU MING-YIN FU HONG-BIN GAO

作者单位: Department of Automation, School of Information science and technology, Beijing Institute of Technology, Beijing, 100081, China

会议类型: 国际会议

会议名称: 2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

会议地点: 香港

会议语种:英文

页码: 383-388

在线出版日期: 2007-08-19（万方平台首次上网日期，不代表论文的发表时间）