Representing image matrices: eigenimages versus eigenvectors

Authors:
Daoqiang Zhang;Songcan Chen;Jun Liu
Affiliations:
Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics and National Laboratory for Novel Software Technology, Nanjing University, Nanjing, China;Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China;Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Year:
2005

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Abstract

We consider the problem of representing image matrices with a set of basis functions. One common solution for that problem is to first transform the 2D image matrices into 1D image vectors and then to represent those 1D image vectors with eigenvectors, as done in classical principal component analysis. In this paper, we adopt a natural representation for the 2D image matrices using eigenimages, which are 2D matrices with the same size of original images and can be directly computed from original 2D image matrices. We discuss how to compute those eigenimages effectively. Experimental result on ORL image database shows the advantages of eigenimages method in representing the 2D images.