Share Email Print
cover

Proceedings Paper

Application of support vector machine and quantum genetic algorithm in infrared target recognition
Author(s): Hongliang Wang; Yangwen Huang; Haifei Ding
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

In this paper, a kind of classifier based on support vector machine (SVM) is designed for infrared target recognition. In allusion to the problem how to choose kernel parameter and error penalty factor, quantum genetic algorithm (QGA) is used to optimize the parameters of SVM model, it overcomes the shortcoming of determining its parameters after trial and error in the past. Classification experiments of infrared target features extracted by this method show that the convergence speed is fast and the rate of accurate recognition is high.

Paper Details

Date Published: 19 August 2010
PDF: 6 pages
Proc. SPIE 7820, International Conference on Image Processing and Pattern Recognition in Industrial Engineering, 78201O (19 August 2010); doi: 10.1117/12.866715
Show Author Affiliations
Hongliang Wang, North Univ. of China (China)
Yangwen Huang, North Univ. of China (China)
Haifei Ding, North Univ. of China (China)


Published in SPIE Proceedings Vol. 7820:
International Conference on Image Processing and Pattern Recognition in Industrial Engineering
Shaofei Wu; Zhengyu Du; Shaofei Wu; Zhengyu Du; Shaofei Wu; Zhengyu Du, Editor(s)

© SPIE. Terms of Use
Back to Top