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Proceedings Paper

Two-class pattern classification using principal component analysis
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Paper Abstract

A two-class classification method of image patterns using principal component analysis (PCA) is proposed, in which classification is performed in the two-dimensional (2-D) space constructed by the reconstruction errors. The reconstruction error is computed using PCA for each assumed class. Training data sets are used to compute eigenvectors with which PCA reduces the dimensionality of the input vector space and reconstructs an input vector in the reduced space. The line equation with two parameters is defined as a linear decision boundary and these parameters are estimated by probabilistic approach. Also its application to face detection is experimented.

Paper Details

Date Published: 8 March 2002
PDF: 9 pages
Proc. SPIE 4664, Machine Vision Applications in Industrial Inspection X, (8 March 2002); doi: 10.1117/12.460192
Show Author Affiliations
Hyoung-Joo Ahn, Sogang Univ. (South Korea)
Rae-Hong Park, Sogang Univ. (South Korea)

Published in SPIE Proceedings Vol. 4664:
Machine Vision Applications in Industrial Inspection X
Martin A. Hunt, Editor(s)

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