NDLI: Linear image coding for regression and classification using the tensor-rank principle

Content Provider	IEEE Xplore Digital Library
Author	Shashua, A. Levin, A.
Copyright Year	2001
Description	Author affiliation: Sch. of Comput. Sci. & Eng., Hebrew Univ., Jerusalem, Israel (Shashua, A.; Levin, A.)
Abstract	Given a collection of images (matrices) representing a "class" of objects we present a method for extracting the commonalities of the image space directly from the matrix representations (rather than from the vectorized representation which one would normally do in a PCA approach, for example). The general idea is to consider the collection of matrices as a tensor and to look for an approximation of its tensor-rank. The tensor-rank approximation is designed such that the SVD decomposition emerges in the special case where all the input matrices are the repeatition of a single matrix. We evaluate the coding technique both in terms of regression, i.e., the efficiency of the technique for functional approximation, and classification. We find that for regression the tensor-rank coding, as a dimensionality reduction technique, significantly outperforms other techniques like PCA. As for classification, the tensor-rank coding is at is best when the number of training examples is very small.
Sponsorship	IEEE Comput. Soc. Tech. Committee on Pattern Analysis & Machine Intelligence
File Size	1053281
File Format	PDF
ISBN	0769512720
ISSN	10636919
DOI	10.1109/CVPR.2001.990454
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-12-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image coding Principal component analysis Matrix decomposition Computer science Face recognition Image recognition Independent component analysis Tensile stress Decorrelation Computer vision
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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