  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.
