Most existing approaches prune wrong matches via estimating an image transformation or solving a graph-based global matching optimization problem, which usually suffers from varying local transformations and outliers. Inspired by the insight that neighboring true matches usually hold consistent local topological structures across images, in this paper we propose a new approach to evaluate the confidence of each putative match based on how well its two keypoints can predict each other by exploring the geometric constraint with its neighboring matches. With the evaluation, a two-stage approach combining recursively false match pruning and correct match incrementing is presented to obtain the reliable matches. Experiments on various image pairs demonstrate that our approach can conduct robust feature matching in challenging conditions and outperform state-of-the-art approaches.

大多数现有方法都是通过估计图像变换或解决基于图的全局匹配优化问题来修剪错误的匹配，而该问题通常会遭受局部变换和离群值变化的困扰。受到洞察力的启发，即相邻的真实匹配项通常在整个图像中都具有一致的局部拓扑结构，在本文中，我们提出了一种新的方法来评估每个推定匹配项的置信度，其依据是两个关键点通过探索几何约束可以相互预测的程度其邻近的比赛。通过评估，提出了一种将递归错误匹配修剪和正确匹配增量相结合的两阶段方法，以获得可靠的匹配。