Seeking reliable correspondences between two feature sets is a fundamental and important task in computer vision. This paper attempts to remove mismatches from given putative image feature correspondences. To achieve the goal, an efficient approach, termed as locality preserving matching (LPM), is designed, the principle of which is to maintain the local neighborhood structures of those potential true matches. We formulate the problem into a mathematical model, and derive a closed-form solution with linearithmic time and linear space complexities. Our method can accomplish the mismatch removal from thousands of putative correspondences in only a few milliseconds. To demonstrate the generality of our strategy for handling image matching problems, extensive experiments on various real image pairs for general feature matching, as well as for point set registration, visual homing and near-duplicate image retrieval are conducted. Compared with other state-of-the-art alternatives, our LPM achieves better or favorably competitive performance in accuracy while intensively cutting time cost by more than two orders of magnitude.

中文翻译：

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位置保持匹配

寻找两个特征集之间的可靠对应关系是计算机视觉中的一项基本且重要的任务。本文试图从给定的假定图像特征对应中去除不匹配。为了实现这一目标，设计了一种称为局部保持匹配 (LPM) 的有效方法，其原理是维护那些潜在真实匹配的局部邻域结构。我们将问题表述为数学模型，并推导出具有线性时间和线性空间复杂度的封闭形式解。我们的方法可以在几毫秒内完成从数千个假定的对应关系中去除不匹配。为了证明我们处理图像匹配问题的策略的普遍性，对用于一般特征匹配的各种真实图像对进行了大量实验，除了点集配准，还进行了视觉归位和近乎重复的图像检索。与其他最先进的替代方案相比，我们的 LPM 在准确性方面实现了更好或具有竞争力的性能，同时将时间成本大幅削减了两个数量级以上。