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.

本文针对在图像检索中几何对应验证，提出了以像素级密集配对检索数据库图像并以全局相似度度量和匹配像素计算的几何一致性度量为基础进行短序列重排的新模型，并通过实验证明了该模型具有竞争力。此外，将该方法应用于长期视觉本地化问题，并在跨数据集上进行了验证。