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Robust image matching using local affine region and Mahalanobis metric

机译：使用局部仿射区域和Mahalanobis度量进行鲁棒的图像匹配

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摘要

Image matching plays an essential role in various computer vision applications. Recent researches found that relative positions among a feature point and its local neighbors can be utilized to build a K Nearest Neighbors (KNN) graph to eliminate the matches with geometric inconsistency. However, the existing KNN graph construction method is unstable under viewpoint changes, as the used Euclidean metric cannot accurately reflect the spatial relationship of feature points. In order to solve this problem, this paper proposes a robust image matching algorithm by using local affine regions and Mahalanobis metric. First, feature points from the images are detected not only with the coordinates but also affine regions around them. Next, feature points and affine information is used to build KNN graph for each image under Mahalanobis metric. Finally, the mismatches are eliminated via finding consensus subgraph. Experimental results demonstrate that the proposed algorithm can build robust KNN graph under large viewpoint changes and achieve higher matching accuracy.
机译：图像匹配在各种计算机视觉应用中起着至关重要的作用。最近的研究发现，可以利用特征点与其局部邻居之间的相对位置来构建K最近邻（K Nearest Neighbors，KNN）图，以消除几何上不一致的匹配项。然而，现有的KNN图构造方法在视点变化下是不稳定的，因为所使用的欧氏度量不能准确反映特征点的空间关系。为了解决这个问题，本文提出了一种利用局部仿射区域和马氏距离度量的鲁棒图像匹配算法。首先，不仅通过坐标检测图像中的特征点，而且还检测图像周围的仿射区域。接下来，特征点和仿射信息用于在Mahalanobis度量下为每个图像构建KNN图。最后，通过找到共识子图来消除不匹配。实验结果表明，该算法可以在较大的视点变化下建立鲁棒的KNN图，并具有较高的匹配精度。

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