摘要
支持向量机方法被看作是对传统学习分类方法的一个好的替代,特别在小样本、高维情况下,具有较好的泛化性能。文章对一对一支持向量机方法进行了改进,并采用其对多目标图像进行了分割研究。实验结果表明,支持向量机方法是一种很有前景的图像分割技术。
Support vector machine approach is considered a good candidate because of its good generalization performance, especially when the number of training samples is very small and the dimension of feature space is very high. In this paper, an improved one-against-one support vector machine is proposed and the segmentation of multi-target image based on the improved one-against-one support vector machine approach is investigated. Experimental results show that support vector machine approach is a promising technique for image segmentation.
出处
《微电子学与计算机》
CSCD
北大核心
2005年第12期51-54,共4页
Microelectronics & Computer
基金
国家自然科学基金的资助(60475024)
关键词
统计学习理论
支持向量机
一对一方法
多目标图像分割
Statistical learning theory, Support vector machine, One-against-one, Segmentation of multi-target image
