Title :
Two-Dimensional Canonical Correlation Analysis
Author :
Lee, Sun Ho ; Choi, Seungjin
Author_Institution :
Pohang Univ. of Sci. & Technol., Kyungbuk
Abstract :
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.
Keywords :
computational complexity; correlation methods; image processing; 2D canonical correlation analysis; 2D-CCA; computational complexity; correspondence learning; image data; Computational complexity; Content based retrieval; Eigenvalues and eigenfunctions; Image analysis; Kernel; Lighting; Stress; Sun; Text mining; Vectors; Canonical correlation analysis (CCA); correspondence learning; two-dimensional analysis;
Journal_Title :
Signal Processing Letters, IEEE
DOI :
10.1109/LSP.2007.896438
