基于改进的一对一支持向量机方法的多目标图像分割  

Segmentation of Multi-target Image Based on Improved One-against-one Support Vector Machine Approach

作　　者：徐海祥[1] 朱光喜[1] 张翔[2] 田金文[2] 彭复员[1] 

机构地区：[1]华中科技大学电子与信息工程系,湖北武汉430074 [2]华中科技大学图像识别与人工智能研究所,湖北武汉430074

出　　处：《微电子学与计算机》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)

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.

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 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]