%0 Journal Article
%T Affine Invariant Feature Extraction Algorithm Based on Generalized Canonical Correlation Analysis<br>基于广义典型相关分析的仿射不变特征提取方法
%A Zhang Jie-yu Chen Qiang Bai Xiao-jing Sun Quan-sen Xia De-shen
%A <br>张洁玉
%A 陈强
%A 白小晶
%A 孙权森
%A 夏德深
%J 电子与信息学报
%D 2009
%I 
%X A novel method of extracting affine invariant feature is proposed using the theory of Generalized Canonical Correlation Analysis(GCCA). First, a new kind of transformation named MSAE is constructed based on MSA. Second, MSAE is proved to be affine invariant. Then MSA is combined with MSAE using GCCA to obtain a new feature with more information. Finally, the coil-100 image database viewed from different angles in the case of Gaussian noise or occlusion is put into recognition experiments using minimum distance classifier. The comparing results among MSA, MSAE and combined feature indicate that the combined feature can obtain highest recognition accuracy followed by MSAE and MSA in turn.
%K Image recognition
%K Multi-Scale Autoconvolution (MSA)
%K Multi-Scale Autoconvolution Entropy (MSAE)
%K Feature fusion
%K Affine invariant feature<br>图像识别
%K 多尺度自卷积
%K 多尺度自卷积熵
%K 特征融合
%K 仿射不变性
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=1319827C0C74AAE8D654BEA21B7F54D3&jid=EFC0377B03BD8D0EF4BBB548AC5F739A&aid=56BB15A354C53B549E44910113BCB2E4&yid=DE12191FBD62783C&vid=4AD960B5AD2D111A&iid=F3090AE9B60B7ED1&sid=4B4A5E2A8D14C2AC&eid=B7BFEB80CBF01858&journal_id=1009-5896&journal_name=电子与信息学报&referenced_num=0&reference_num=31