摘要
为了有效提高图像分类的准确率,充分利用图像本身的结构信息并压缩图像数据,首先构造三阶图像特征张量,利用非负矩阵分解(NMF)在张量子空间降维,提出了一种基于二维主成分分析(2DPCA)来得到NMF初始点的方法,保证了图像信息的有效利用.然后,为了保持降维后的张量子空间所在的流形空间的本征结构,根据图像类标构造权值矩阵,并把图像集合构造成四阶张量实现图像的分类.通过对两个图像数据库的实验,表明该方法能有效提升图像分类的准确率.
To improve the accuracy of image classification ,fully use the structural information of the data ,and compress the image data , first , third order tensor image features are constructed . Then , non-negative matrix factorization （NM F） is used for dimension reduction .A method of choosing a good starting point is proposed using two-dimensional principal component analysis （2DPCA） ,which uses the information of the image effectively .Next , in order to maintain the intrinsic structure of the manifold for tensor subspace ,weighted matrix is derived according to the labels of images .Meanwhile the set of images is used to construct a fourth order tensor .A method for classification is proposed by using weighted high-order singular value decomposition for support tensor machine .The experimental results on two image databases show that the proposed can effectively improve the accuracy of image classification .
出处
《微电子学与计算机》
CSCD
北大核心
2014年第5期28-31,共4页
Microelectronics & Computer
基金
国家自然科学基金项目(61073116)
高校省级优秀青年人才基金重点项目(2011SQRL129ZD)
关键词
高阶奇异值分解
非负矩阵分解
支持张量机
二维主成分分析
high-order singular value decomposition
non-negative matrix [actorization
support tensor machine
two-dimensional principal component analysis
