We propose a novel method for template matching in unconstrained environments. Its essence is the Best-Buddies Similarity (BBS), a useful, robust, and parameter-free similarity measure between two sets of points. BBS is based on counting the number of Best-Buddies Pairs (BBPs)-pairs of points in source and target sets that are mutual nearest neighbours, i.e., each point is the nearest neighbour of the other. BBS has several key features that make it robust against complex geometric deformations and high levels of outliers, such as those arising from background clutter and occlusions. We study these properties, provide a statistical analysis that justifies them, and demonstrate the consistent success of BBS on a challenging real-world dataset while using different types of features.

中文翻译：

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最好的伙伴相似性__使用相互最近邻居的强大模板匹配


我们提出了一种在无约束环境中进行模板匹配的新方法。其本质是 Best-Buddies 相似度 (BBS)，这是两组点之间有用、稳健且无参数的相似性度量。 BBS 基于对最佳好友对 (BBP) 的数量进行计数，即源集中和目标集中相互最近邻的点对，即每个点都是另一个点的最近邻。 BBS 具有几个关键功能，使其能够抵御复杂的几何变形和高水平的异常值，例如由背景杂乱和遮挡引起的异常值。我们研究这些属性，提供统计分析来证明它们的合理性，并证明 BBS 在使用不同类型的特征时在具有挑战性的现实世界数据集上的持续成功。