基于改进的支持向量机方法的多目标图像分割  

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

作　　者：徐海祥[1,2] 曹万华[1,2] 陈炜[2] 郭丽艳[2] 

机构地区：[1]哈尔滨工程大学计算机科学与技术学院 [2]武汉数字工程研究所

出　　处：《舰船电子工程》2009年第2期113-115,共3页Ship Electronic Engineering

摘　　要：支持向量机方法被看作是对传统学习分累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。针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 as 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：TP391.41[自动化与计算机技术—计算机应用技术]