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| Affine Invariant Feature Extraction Algorithm Based on Generalized Canonical Correlation Analysis |
| Zhang Jie-yu; Chen Qiang; Bai Xiao-jing; Sun Quan-sen; Xia De-shen |
| Department of Computer Science & Technology, Nanjing University of Science & Technology, Nanjing 210094, China |
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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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Received: 14 October 2008
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2009, Vol. 31 
