Segmentation of images using support vector machines

被引：2
|
作者：
Chen, QY [1 ]
Yang, Q [1 ]
机构：
[1] Chengdu Univ Technol, Chengdu 610059, Peoples R China
来源：
PROCEEDINGS OF THE 2004 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7 | 2004年
关键词：
image segmentation; SVMs; radial basis function kernel;
D O I：
10.1109/ICMLC.2004.1378608
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Support vector machines (SVMs) 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.
引用
收藏
页码：3304 / 3306
页数：3
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