Abstract
This paper addresses the problem of determining dense pixel correspondences between two images and its application to geometric correspondence verification in image retrieval. The main contribution is a geometric correspondence verification approach for re-ranking a shortlist of retrieved database images based on their dense pair-wise matching with the query image at a pixel level. We determine a set of cyclically consistent dense pixel matches between the pair of images and evaluate local similarity of matched pixels using neural network based image descriptors. Final re-ranking is based on a novel similarity function, which fuses the local similarity metric with a global similarity metric and a geometric consistency measure computed for the matched pixels. For dense matching our approach utilizes a modified version of a recently proposed dense geometric correspondence network (DGC-Net), which we also improve by optimizing the architecture. The proposed model and similarity metric compare favourably to the state-of-the-art image retrieval methods. In addition, we apply our method to the problem of long-term visual localization demonstrating promising results and generalization across datasets.
Abstract (translated)
本文讨论了两幅图像之间密集像素对应关系的确定问题及其在图像检索中几何对应关系验证中的应用。主要贡献是一种几何对应验证方法，根据检索到的数据库图像与查询图像在像素级的密集配对匹配，对其进行重新排序。我们确定了一组图像之间的循环一致密集像素匹配，并利用基于神经网络的图像描述符评估匹配像素的局部相似性。最后的重新排序是基于一个新的相似度函数，它将局部相似度指标与全局相似度指标以及匹配像素的几何一致性指标融合在一起。对于密集匹配，我们的方法使用了一个最近提出的密集几何对应网络（DGC网络）的修改版本，我们还通过优化架构来改进该网络。与目前最先进的图像检索方法相比，本文提出的模型和相似性度量方法具有更好的优越性。此外，我们还将我们的方法应用于长期的视觉定位问题，显示出有希望的结果和跨数据集的泛化。
URL
https://arxiv.org/abs/1904.06882
