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。

通过密集像素匹配进行几何图像对应验证

Geometric Image Correspondence Verification by Dense Pixel Matching

We propose a fast, accurate matching method for estimating dense pixel
correspondences across scenes. It is a challenging problem to estimate dense
pixel correspondences between images depicting different scenes or instances of
the same object category. While most such matching methods rely on hand-crafted
features such as SIFT, we learn features from a large amount of unlabeled image
patches using unsupervised learning. Pixel-layer features are obtained by
encoding over the dictionary, followed by spatial pooling to obtain patch-layer
features. The learned features are then seamlessly embedded into a multi-layer
match- ing framework. We experimentally demonstrate that the learned features,
together with our matching model, outperforms state-of-the-art methods such as
the SIFT flow, coherency sensitive hashing and the recent deformable spatial
pyramid matching methods both in terms of accuracy and computation efficiency.
Furthermore, we evaluate the performance of a few different dictionary learning
and feature encoding methods in the proposed pixel correspondences estimation
framework, and analyse the impact of dictionary learning and feature encoding
with respect to the final matching performance.

通过无监督学习从大量未标记图像路径中学习特征，对像素进行编码，并进行空间池化以获得补丁层特征，然后将学习到的特征顺畅地嵌入到多层匹配框架中，得到了与最先进的方法相比在准确性和计算效率方面均优于 SIFT flow 等的匹配模型，并分析了字典学习和特征编码对最终匹配性能的影响。