基于支持向量机方法的噪声图像分割  

Segmentation of Images Corrupted by Noise Based on Fuzzy Weighted Support Vector Machine Approach

作　　者：徐海祥 夏学知[1] 陈炜[1] 郭丽艳[1] 

机构地区：[1]武汉数字工程研究所

出　　处：《微电子学与计算机》2007年第11期14-16,20,共4页Microelectronics & Computer

摘　　要：图像分割是计算机视觉领域的关键技术之一。支持向量机方法被认为是好的学习分类方法之一,特别在小样本、高维情况下,具有较好的泛化性能。针对噪声图像的分割,提出了模糊权重支持向量机方法。分割实验表明,与经典支持向量机方法相比,模糊权重支持向量机方法具有更强的抗噪性。Image segmentation is critical to computer vision. 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. The presented paper proposes the fuzzy weighted support vector machine approach for segmentation of images corrupted by noise. Experimental results show that the fuzzy weighted support vector machine approach is more robust than classical support vector machine approach.

关 键 词：支持向量机 噪声图像分割 计算机视觉 统计学习理论 

分 类 号：TP91[自动化与计算机技术]