基于改进协方差矩阵的半色调图像分类研究  

Study on halftone image classification based on the improved covariance matrix

作　　者：钟智彦 文志强[1] 叶德刚[1] 

机构地区：[1]湖南工业大学计算机与通信学院,湖南株洲412007

出　　处：《微型机与应用》2015年第19期54-57,共4页Microcomputer & Its Applications

基　　金：湖南省研究生科研创新项目(CX2015B566)

摘　　要：针对半色调图像分类中只存在0和1的特点,提出了一种基于改进的协方差矩阵在半色调图像中的分类方法。根据协方差矩阵在实现半色调图像分类中个数少且并未体现其局部和全局信息的特性,对协方差矩阵的底层特征进行改进。利用样本的局部特性和核密度估计方法,实现黎曼流形上的贝叶斯分类策略。实验中研究协方差矩阵的底层特征与传统协方差矩阵的特征提取方法并对其进行分类性能比较。实验结果表明,在半色调图像分类中,与传统的协方差矩阵相比较,改进的协方差矩阵提取出的特征在分类中平均错误分类率更低。For the characteristic of halftone image classification that only has two values 0 and 1, this paper proposes an improved covariance matrix classification method based on halftone image. According to the covariance matrix in the realization of a halftone image classification does not reflect the less number and its local and global information on the characteristics, the underlying features of the covariance matrix is improved. Local features of samples and kernel density estimation method are used to achieve Bayesian classification strategy Riemannian manifolds. Experimental study of low-level features of the covariance matrix of the covariance matrix of features with traditional extraction methods to classify performance comparison. Experimental results show that the traditional covariance matrix comparing the halftone image classification and improved covariance matrix extracted feature less in the category average misclassification rate.

关 键 词：半色调图像 协方差矩阵 黎曼流形 最近邻分类器 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]