Letters: An affine invariant discriminate analysis with canonical correlation analysis

Authors:
Rushi Lan;Jianwei Yang;Yong Jiang;Zhan Song;Yuan Yan Tang
Affiliations:
College of Math and Statistics, Nanjing University of Information Science and Technology, Nanjing 210044, China;College of Math and Statistics, Nanjing University of Information Science and Technology, Nanjing 210044, China;College of Math and Statistics, Nanjing University of Information Science and Technology, Nanjing 210044, China;Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China and The Chinese University of Hong Kong, Hong Kong, China;Department of Computer Science, Chongqing University, Chongqing 400030, China
Venue:
Neurocomputing
Year:
2012

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Abstract

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.