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Two-dimensional canonical correlation analysis
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DOI：10.1109/LSP.2007.896438.png)
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In this letter, we present a method of two-dimensional canonical correlation analysis (2D-CCA) where we extend the standard CCA in such a way that relations between two different sets of image data are directly sought without reshaping images into,vectors. We stress that 2D-CCA dramatically reduces the computational complexity, compared to the standard CCA. We show the useful behavior of 2D-CCA through numerical examples of correspondence learning between face images in different poses and illumination conditions.
Keyword:
canonical correlation analysis (CCA)
correspondence learning
two-dimensional analysis
期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
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EcologyIF0
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