针对传统基于互信息图像匹配算法计算量大，且没有考虑像素空间关系和效用的问题，提出了一种基于定量定性互信息的多层次特征图像匹配算法：首先对边缘提取后的图像提取多层次特征，即边缘兴趣点、边缘点和边缘邻域点特征；然后基于不同特征点特性，计算定量定性互信息联合效用；最后在遗传算法框架下，将定量定性互信息值作为适应度函数值搜索匹配参数。仿真结果表明，本文提出的匹配算法精度高，耗时较短且对噪声不敏感。

Conventional image matching algorithms based on mutual information not only consume large amounts of time, but also ignore the pixels’ utilities and spatial relations. In this paper, a novel image matching algorithm using multilevel features is proposed based on quantitative-qualitative measure of mutual information(Q-MI). Firstly, multilevel features are extracted on the edge image, including edge points of interest, edge points and edge neighborhood points. Secondly, according to the characteristics of multilevel features, the Q-MI joint utility for each pixel value pair is computed. Lastly, an optimizer based genetic algorithm(GA) is applied to effectively search the best matching transformation parameters, with Q-MI as the fitness function. Experimental results demonstrate the accuracy, efficiency and robustness of this algorithm.