基于支持向量机的景象匹配区选择方法

Support Vector Machine for Scene Matching Area Selection

考虑景象匹配区选择结果与参考图像信息之间的联系与制约,提出了基于支持向量机的景象匹配区选择方法.首先合理选择基于灰度和基于特征的2级测度参数,准确有效地表示参考图图像信息;其次将多个测度参数归一化组成样本集的输入属性向量,并使用去均值归一化算法得到匹配结果作为样本集的输出;最后选择合适的核函数与最佳参数训练样本集数据,得到具有自动区分景象适配区与非景象匹配区能力的决策函数,并用其分类实现任意候选参考图的匹配区选择.实验结果表明该方法具有较大的适应性和抗干扰性,能够对复杂参考图的匹配区选择进行正确决策指导.

In view of interrelations and constraints of the result of selecting matching area and the information of reference image, the paper presents a method for selecting suitable scene matching area by using support vector machine. First, intensity-based and feature-based measure parameters of reference image were selected to represent the reference image＇s information accurately and effectively. Then the measured parameters were coded as input feature vectors of training set and matching results were gained as output of training set by normalized cross-correlation algorithm. Finally, after the training with appropriate radial basis kernel function and corresponding optimal parameters, decision function which could separate suitable and unsuitable matching area class was obtained. Thus, matching area can be extracted from random reference image by decision function. The experimental results show that this method holds the capability of flexibility and jamming resistance as well as proper guide to selection of suitable matching area from complex reference image