%0 Journal Article
%T Application of PCA in dimension reduction of image Zernike moments feature<br>主成分分析在图像Zernike矩特征降维中的应用
%A LIU Mao-fu
%A HU Hui-jun
%A HE Yan-xiang
%A <br>刘茂福
%A 胡慧君
%A 何炎祥
%J 计算机应用
%D 2007
%I 
%X Higher dimension of image feature is the critical problem and the dimension reduction is the most important phase in image processing. It was pointed out that the dimension of Zernike moments feature vector was generally high after briefly introducing the basic concept of the Zernike moments and the image Zernike moments shape feature vector. Based on the principal components analysis, it was shown that the principal components analysis (PCA) could be applied in dimension reduction of image Zernike moments feature. Meanwhile, the process of the dimension reduction based on PCA was put forward. The experimental results demonstrate the feasibility of the application.
%K Zernike moments
%K feature vector
%K principal components analysis (PCA)
%K dimension reduction<br>Zernike矩
%K 特征向量
%K 主成分分析
%K 降维
%K 主成分分析方法
%K 图像特征
%K Zernike
%K moments
%K 特征降维
%K 应用
%K feature
%K image
%K dimension
%K reduction
%K 结果
%K 实验
%K 处理过程
%K 矩特征向量
%K 向量表示
%K 形状特征
%K 降维处理
%K 问题
%K 向量维度
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=831E194C147C78FAAFCC50BC7ADD1732&aid=14E16CBD2743051227314ECD4F4F2F6B&yid=A732AF04DDA03BB3&vid=DB817633AA4F79B9&iid=38B194292C032A66&sid=A7379F6713A46835&eid=6CDD207A90CE1EEC&journal_id=1001-9081&journal_name=计算机应用&referenced_num=0&reference_num=9