DocumentCode
2296131
Title
Segmentation of images using support vector machines
Author
Chen, Qian-Ying ; Yang, Qiang
Author_Institution
Chengdu Univ. of Technol., China
Volume
5
fYear
2004
fDate
26-29 Aug. 2004
Firstpage
3304
Abstract
Support vector machine (SVM) is a general algorithm based on guaranteed risk bounds of statistical learning theory. They have found numerous applications, such as in face recognition, character recognition, face detection and so on. In this paper, we propose the use of SVMs to image segmentation. The keystone that we research is how to choice the feature set for SVMs in this paper. We demonstrate that appropriate feature subset is very important to the generality capability of SVMs.
Keywords
feature extraction; image segmentation; statistical analysis; support vector machines; SVM; character recognition; face detection; face recognition; feature subset; image segmentation; statistical learning theory; support vector machines; Algorithm design and analysis; Application software; Character recognition; Educational institutions; Face detection; Face recognition; Image segmentation; Statistical learning; Support vector machines; Training data;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on
Print_ISBN
0-7803-8403-2
Type
conf
DOI
10.1109/ICMLC.2004.1378608
Filename
1378608
Link To Document