Multi-scale image segmentation algorithm based on support vector machine approximation criteria

被引:3
|
作者
Wang, Liejun [1 ]
Jia, Zhenhong [1 ]
机构
[1] Xinjiang Univ, Coll Informat Sci & Engn, Urumqi, Peoples R China
来源
关键词
support vector machine; multi-scale image segmentation; multi-resolution analysis; SVM;
D O I
10.1002/cpe.1893
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
A new multi-scale image segmentation algorithm based on support vector machine (SVM) approximation criteria has been discussed in this paper. Most current multi-scale image segmentation algorithms are based on the restricted empirical risk minimization, and the approximation of multi-scale image segmentation was poor. As the SVM theory was one based on the structural risk minimization, the best approximation results could be reached. So, it was combined with multi-scale image segmentation algorithms, and one-image multi-resolution analysis approximation algorithms based on the SVM theory were presented in this paper, which could obtain more accurate multi-scale image segmentation. By numerical results, the algorithm was further verified that more accurate image segmentation results were unfolded. Copyright (C) 2011 John Wiley & Sons, Ltd.
引用
收藏
页码:1231 / 1238
页数:8
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