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中文摘要：
针对图像匹配问题，提出了一种图像多阶特征对集的最优匹配模型．图像的多阶特征主要是指一阶、二阶和三阶特征，分别由单个特征点、特征点之间的边或者连接特征点的三角形来定义．最优匹配模型是一个以图像多阶特征为顶点集的加权二分图，其优点是权重参数可以直接计算，并能采用Kuhn—Munkras算法求解最大权对集．实验结果表明，该模型具有很好的鲁棒性，对于视频序列图像和涂鸦图像，即使在存在较大缩放、旋转和仿射变换的情况下，也能获得比较精确的匹配结果，其准确度通常优于OpenCV中著名的Flann和BruteForce匹配算法．
英文摘要：
An optimal correspondence model was proposed for solving image matching problems with multi-order features. A multi-order feature of an image refers to any of its first-, second- and third-order feature, which was defined by a simple feature point, an edge linking two feature points and a triangle connecting three feature points, respectively. The optimal correspondence model was a weighted bipartite graph with multi-order feature as its vertex. With this model the weight could be directly computed and the solution can be easily obtained by the Kuhn-Munkras algorithm. Results show that the model has good robustness for video sequence and graffiti images. Even with obvious rotation, scale, and affine transformation, it can produce a relatively accurate correspondence result, which is usually better than the famous Flann and BruteForce algorithms in OpenCV.
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