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

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.