正交全局-局部判别映射应用于植物叶片分类  

Orthogonal global-locally discriminant projection for plant leaf classification

作　　者：张善文[1,2] 巨春飞[1] 

机构地区：[1]西京学院工程技术系,西安710123 [2]中国科学院合肥机械研究所,合肥230031

出　　处：《农业工程学报》2010年第10期162-166,共5页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金项目(60975005)

摘　　要：提出了一种正交全局-局部判别映射的维数约简方法,并应用于植物叶片分类中。对于给定点的邻域点集,首先建立能够描述数据点之间关系的权重矩阵;然后,充分利用数据的类别信息和局部信息来构建类间散度矩阵和局部结构矩阵,使得映射后类内数据点之间的距离减小,而类间数据点之间的距离增大,这一性质有利于数据分类;最后,构造正交优化目标函数,通过Lagrange数乘法求解该目标函数。植物叶片图像分类的试验结果表明,该方法是有效、可行的。A dimensional reduction method named orthogonal ＇global-locally＇ discriminant projection（OGLDP） was proposed for plant leaf classification in this paper.Given a set of data points in the ambient space,a weight matrix was firstly built which describes the relationship between the data points.Then the between-class scatter matrix and locally structure matrix were constructed by making use of the class information and locally information of the data,which can ＇push＇ the within-class data points closer together,while simultaneously ＇pull＇ the between-class data points even more far from each other.This character is advantage to data classification.Finally,the optimal objective function was set up,which was solved by Lagrange multiplication.The experiment results of plant leaf classification show that OGLDP is effective and feasible.

关 键 词：流形学习 植物叶片分类 正交全局-局部判别映射 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]