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Proceedings Paper

Comparison Of Eigenvector-Based Statistical Pattern Recognition Algorithms For Hybrid Processing
Author(s): Q. Tian; Y. Fainman; Sing H. Lee
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Paper Abstract

The pattern recognition algorithms based on eigenvector analysis (group 2) are theoretically and experimentally compared in this part of the paper. Group 2 consists of Foley-Sammon (F-S) transform, Hotelling trace criterion (HTC), Fukunaga-Koontz (F-K) transform, linear discriminant function (LDF) and generalized matched filter (GMF). It is shown that all eigenvector-based algorithms can be represented in a generalized eigenvector form. However, the calculations of the discriminant vectors are different for different algorithms. Summaries on how to calculate the discriminant functions for the F-S, HTC and F-K transforms are provided. Especially for the more practical, underdetermined case, where the number of training images is less than the number of pixels in each image, the calculations usually require the inversion of a large, singular, pixel correlation (or covariance) matrix. We suggest solving this problem by finding its pseudo-inverse, which requires inverting only the smaller, non-singular image correlation (or covariance) matrix plus multiplying several non-singular matrices. We also compare theoretically the effectiveness for classification with the discriminant functions from F-S, HTC and F-K with LDF and GMF, and between the linear-mapping-based algorithms and the eigenvector-based algorithms. Experimentally, we compare the eigenvector-based algorithms using a set of image data bases each image consisting of 64 x 64 pixels.

Paper Details

Date Published: 8 February 1989
Proc. SPIE 0960, Real-Time Signal Processing for Industrial Applications, (8 February 1989); doi: 10.1117/12.947801
Show Author Affiliations
Q. Tian, University of California (United States)
Y. Fainman, University of California (United States)
Sing H. Lee, University of California (United States)

Published in SPIE Proceedings Vol. 0960:
Real-Time Signal Processing for Industrial Applications
Bahram Javidi, Editor(s)

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