Comparison Of Eigenvector-Based Statistical Pattern Recognition Algorithms For Hybrid Processing

Q. Tian; Y. Fainman; Sing H. Lee

doi:10.1117/12.947801

8 February 1989 Comparison Of Eigenvector-Based Statistical Pattern Recognition Algorithms For Hybrid Processing

Q. Tian, Y. Fainman, Sing H. Lee

Proceedings Volume 0960, Real-Time Signal Processing for Industrial Applications; (1989) https://doi.org/10.1117/12.947801
Event: SPIE International Symposium on Optical Engineering and Industrial Sensing for Advance Manufacturing Technologies, 1988, Dearborn, MI, United States

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.

Citation Download Citation

Q. Tian, Y. Fainman, and Sing H. Lee "Comparison Of Eigenvector-Based Statistical Pattern Recognition Algorithms For Hybrid Processing", Proc. SPIE 0960, Real-Time Signal Processing for Industrial Applications, (8 February 1989); https://doi.org/10.1117/12.947801

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Detection and tracking algorithms

Matrices

Image classification

Pattern recognition

Algorithms

Silicon

Signal processing

Show All Keywords

Keywords/Phrases

Search In:

Publication Years