为了对图像中发生平移、伸缩及旋转等形变的目标进行有效检测和跟踪，提出了基于复数小波能量特征和支持向量机的图像匹配算法，以便把图像匹配问题转化为图像分类问题。该算法首先利用复数小波的方向选择性、多尺度特性及近似平移不变性来抽取图像能量的均值、均方差及偏度等统计特征，并将其作为支持向量机的输入参数，用于训练模板样本集合，以获得支持向量，然后对由输入图像构成的与模板大小相同的所有子图像进行测试。这是一个粗精结合的两步算法，即先运用支持向量机筛选出侯选目标集合，再运用非线性距离判优准则来确定检测出的候选目标图像集合中的最优匹配。实验结果表明，该算法克服了传统图像匹配方法搜索目标时存在的置信度问题，通过与基于径向基的神经网络学习方法和基于灰度相关的匹配方法比较可见，该算法在性能上优于这两个方法，并能得到满意的匹配结果。

This algorithm, which is based on both statistical characteristics of complex wavelet energy and SVM, is proposed in order to effectively detect and track targets in image, which may cause changes, such as translation, scaling and rotation. So, the problem of image matching is transformed as that of classification. The transformation of complex wavelet that has properties of scale, shift invariant and directional selectivity effectively extract the statistical characteristics of image, such as mean, standard deviation and skew. The statistical characteristics of sample templates are input into SVM to train support vectors of SVM. Then, those statistical characteristics of any sub-image from original image are input into SVM in order to match target. This is a two-stage algorithm of coarse-to-fine. Firstly, the set of candidates is sifted by SVM. Secondly, a new optimal rule, which is nonlinear distance function, is proposed to decide the optimal matching from the candidate set. Those experimental results show that this algorithm addresses the problem of confidence level, which generally exists in traditional matching methods. This algorithm's performance is superior to those of both learning method of neural network based on RBF and gray-level correlation matching method, which compares with them. Finally, a good matching result is obtained.