基于粒子群优化的核匹配追踪目标识别(英文)  

Object Recognition Using Particle Swarm Optimization based Kernel Matching Pursuit

作　　者：徐小慧[1,2] 魏鑫[3] 张安[2] 

机构地区：[1]中国人民解放军驻114厂军代表室,西安710077 [2]西北工业大学电子信息学院,西安710072 [3]中国人民解放军驻西飞公司军代表室,西安710089

出　　处：《光子学报》2009年第4期992-996,共5页Acta Photonica Sinica

基　　金：Supported by Natural Science Fundation of Shaanxi Province(2006F45)

摘　　要：提出了一种基于粒子群优化的用于目标识别的核匹配追踪算法.该算法用粒子群优化算法在基函数字典中选择最优的基函数,大大降低了基匹配追踪算法的计算复杂度.通过与标准核匹配追踪算法及基于遗传算法的核匹配追踪算法对UCI数据集及纹理图像的识别试验表明,核匹配追踪算法优良的分类性能以及粒子群优化算法高效的全局搜索能力使新算法能有效识别目标数据.A method for object recognition using Kernel matching pursuits based on particle swarm optimization technique is presented. By using the particle swarm optimization algorithm in search basic function data in function dietionary, the Kernel matching pursuits method can reduces computational complexity of basic matehing pursuit algorithm. As compared with kernel matching pursuits in UCI database and texture image recognition, the proposed algorithm can recognize desired object effectively. This owes to the fact that the kernel matching pursuits is a good classification method and Particle Swarm Optimization algorithm makes use of the global search ability to get the best matching signal structure.

关 键 词：目标识别 核匹配追踪 粒子群优化 光学信息处理 进化算法 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构] TN911.7[自动化与计算机技术—计算机科学与技术]