DocumentCode
2520348
Title
A new method of target recognition based on Rough Set and Support Vector Machine
Author
Guo, Zhi-Jun ; He, Xin ; Wei, Zhong-hui ; Liang, Guo-long
Author_Institution
Changchun Inst. of Opt. Fine Mech. & Phys., Chinese Acad. of Sci., Changchun, China
fYear
2010
fDate
9-11 April 2010
Firstpage
570
Lastpage
574
Abstract
Automatic target recognition (ATR) is an important task in image application. This paper concentrates on two key subroutines of ATR system: Pre-treatment and design of classifier. In the pre-treatment subroutine, a new method based on Rough Set (RS) is proposed to partition the original sample set into some subsets and calculate their class membership, so that some samples can be chosen by class membership to be trained. After pre-treatment, an iterative algorithm based on Rough Set and Support Vector Machines (IRSSVM) is introduced to design a classifier for recognizing two types of targets. The experiment results show that IRSSVM needs less training time and the classifier is simpler and has more generalization and higher recognition rate.
Keywords
image recognition; iterative methods; object detection; pattern classification; rough set theory; support vector machines; automatic target recognition; class membership; classifier design; image application; iterative algorithm; pre-treatment; recognition rate; rough set theory; subsets; support vector machine; training time; Algorithm design and analysis; Data mining; Fuzzy set theory; Helium; Iterative algorithms; Physics; Set theory; Support vector machine classification; Support vector machines; Target recognition; class membership; data partition; fuzzy theory; iterative algorithm; rough set; sample sorting; support vector machine; target recognition;
fLanguage
English
Publisher
ieee
Conference_Titel
Image Analysis and Signal Processing (IASP), 2010 International Conference on
Conference_Location
Zhejiang
Print_ISBN
978-1-4244-5554-6
Electronic_ISBN
978-1-4244-5556-0
Type
conf
DOI
10.1109/IASP.2010.5476053
Filename
5476053
Link To Document