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An adaptive image feature matching method using mixed Vocabulary-KD tree

机译：混合词汇kd树的自适应图像特征匹配方法

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摘要

This paper proposes an adaptive scale-invariant feature matching method based on data clustering, to solve the problem of low robustness of the KD tree matching method caused by SIFT feature noise sensitivity, and our method can be used to AR applications. The method has two stages: offline data re-clustering and online two-stage feature matching. This paper is the first to present a Vocabulary-KD data structure which achieves SIFT using KD tree by tuning the number of features of the Vocabulary nodes. Moreover, based on the Vocabulary-KD data structure, an adaptive feature matching method is proposed, which is consist of two clustering, one on the feature sets and the other on the feature sets contained by the leaf nodes of the Vocabulary-KD tree, along with adaptive adjustment of the relevant parameters of the Vocabulary-KD tree. At last, key images are selected in real-time for the second stage feature matching. The different results show that the proposed method can effectively resist noise, improve the adaptivity of the SIFT feature matching method, so as to achieve the trade-off between efficiency and robustness.
机译：本文提出了一种基于数据聚类的自适应级别不变特征匹配方法，解决了筛选特征噪声灵敏度引起的KD树匹配方法的低稳健性问题，我们的方法可用于AR应用。该方法有两个阶段：脱机数据重新聚类和在线两级特征匹配。本文是第一个呈现词汇 - KD数据结构，通过调整词汇节点的特征数来使用KD树实现S SIFT。此外，基于词汇kd数据结构，提出了一种自适应特征匹配方法，该方法由两个聚类组成，其中一个在特征集上，另一个在词汇表-kd树的叶节点包含的特征集上，以及对词汇-KD树的相关参数的自适应调整。最后，在第二阶段特征匹配中实时选择密钥图像。不同的结果表明，该方法可以有效地抵抗噪声，提高筛选特征匹配方法的适应性，从而在效率和鲁棒性之间实现权衡。

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