An affine invariant discriminate analysis with canonical correlation analysis

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• 作者：Rushi Lan^a  ; [Author Vitae] ; Jianwei Yang^a ; ^{yjianw2002@yahoo.com.cn}  ; [Author Vitae] ; Yong Jiang^a  ; [Author Vitae] ; Zhan Song^b ; ^c  ; [Author Vitae] ; Yuan Yan Tang^d  ; [Author Vitae]
• 关键词：Affine invariant discriminate analysis (AIDA) ; Canonical correlation analysis (CCA) ; Central projection transform (CPT) ; Affine transformation
• 刊名：Neurocomputing
• 出版年：2012
• 期刊代码：95_09252312
• 类别：cp
• 出版时间：1 June, 2012
• 卷：86
• 期：Complete
• 页码：184-192
• 文件大小：886 K

摘要

Canonical correlation analysis (CCA) is invariant with regard to affine transformation, but it cannot be directly applied to affine invariant pattern recognition. The reason mainly lies in that many existing CCA-based schemes represent the pattern by matrix-to-vector method, as a result, the structure and spatial information of the original pattern is discarded. In this paper, an affine invariant discriminate analysis (AIDA) method is developed for pattern recognition. Dislike the matrix-to-vector representation, an object is first converted to a projection matrix by central projection transform (CPT). After a point matching process, CCA is performed to projection matrices of the object and the model, and two vectors will be derived. Therefore, the object is classified to a model by the smallest distance between the obtained vectors. Comparisons of experimental results are given with respect to some existing methods, which demonstrate the effectiveness of the proposed AIDA method.