基于广义典型相关分析的仿射不变特征提取方法

张洁玉; 陈强; 白小晶; 孙权森; 夏德深

doi:10.3724/SP.J.1146.2008.01344

基于广义典型相关分析的仿射不变特征提取方法

doi: 10.3724/SP.J.1146.2008.01344

基金项目:

国家自然科学基金(60773172)和江苏省博士后基金(AD41158)资助课题

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出版历程
- 收稿日期: 2008-10-14
- 修回日期: 2009-03-23
- 刊出日期: 2009-10-19

Affine Invariant Feature Extraction Algorithm Based on Generalized Canonical Correlation Analysis

摘要

摘要: 该文结合广义典型相关分析(GCCA)理论，提出了一种新的图像仿射不变特征提取方法。首先，基于多尺度自卷积变换(MSA)构造了一组新的变换量多尺度自卷积熵(MSAE)。然后证明了该熵具有仿射不变性；再利用GCCA将MSA和MSAE变换值作为两种特征进行融合，得到具有更丰富图像信息的组合特征。最后利用MSA，MSAE和组合特征，结合最近距离分类器分别对视点变换图像以及加噪声、加部分遮挡视点变换图像进行分类识别实验。结果表明，组合特征得到了最高的正确识别率，MSAE次之，MSA最低。
- 图像识别; 多尺度自卷积; 多尺度自卷积熵; 特征融合; 仿射不变性
Abstract: 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.

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