Fast Multi-image Matching via Density-Based Clustering
Fast Multi-image Matching via Density-Based Clustering
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DOI：
10.1109/iccv.2017.437
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发表时间：
2017-10
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通讯作者：
Roberto Tron;Xiaowei Zhou;Carlos Esteves;Kostas Daniilidis
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文献类型：
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作者：
Roberto Tron;Xiaowei Zhou;Carlos Esteves;Kostas Daniilidis
我们考虑在多个图像中找到一致匹配的问题。当前最先进的解决方案使用对匹配循环的约束以及凸优化，导致计算密集型迭代算法。在本文中，我们提出了一个基于聚类的公式：我们首先严格显示其与传统方法的等价性，然后提出QuickMatch，一种新的算法，识别多图像匹配的密度函数在特征空间。具体来说，QuickMatch使用密度估计对树中的点进行排序，然后通过使用特征距离和独特性度量来打破这棵树来提取匹配。我们的算法在准确性上优于以前最先进的方法（如MatchALS），并且速度明显更快（在某些基准测试中快达62倍），并且可以扩展到大型数据集（具有超过2万个特征）。
We consider the problem of finding consistent matches across multiple images. Current state-of-the-art solutions use constraints on cycles of matches together with convex optimization, leading to computationally intensive iterative algorithms. In this paper, we instead propose a clustering-based formulation: we first rigorously show its equivalence with traditional approaches, and then propose QuickMatch, a novel algorithm that identifies multi-image matches from a density function in feature space. Specifically, QuickMatch uses the density estimate to order the points in a tree, and then extracts the matches by breaking this tree using feature distances and measures of distinctiveness. Our algorithm outperforms previous state-of-the-art methods (such as MatchALS) in accuracy, and it is significantly faster (up to 62 times faster on some benchmarks), and can scale to large datasets (with more than twenty thousands features).