Visual Place recognition is commonly addressed as an image retrieval problem.
However, retrieval methods are impractical to scale to large datasets, densely
sampled from city-wide maps, since their dimension impact negatively on the
inference time. Using approximate nearest neighbour search for retrieval helps
to mitigate this issue, at the cost of a performance drop. In this paper we
investigate whether we can effectively approach this task as a classification
problem, thus bypassing the need for a similarity search. We find that existing
classification methods for coarse, planet-wide localization are not suitable
for the fine-grained and city-wide setting. This is largely due to how the
dataset is split into classes, because these methods are designed to handle a
sparse distribution of photos and as such do not consider the visual aliasing
problem across neighbouring classes that naturally arises in dense scenarios.
Thus, we propose a partitioning scheme that enables a fast and accurate
inference, preserving a simple learning procedure, and a novel inference
pipeline based on an ensemble of novel classifiers that uses the prototypes
learned via an angular margin loss. Our method, Divide&Classify (D&C), enjoys
the fast inference of classification solutions and an accuracy competitive with
retrieval methods on the fine-grained, city-wide setting. Moreover, we show
that D&C can be paired with existing retrieval pipelines to speed up
computations by over 20 times while increasing their recall, leading to new
state-of-the-art results.

本文提出了一种称为 Divide＆Classify（D＆C）的方法，它将视觉场所识别问题作为一个分类问题来解决，以避免相似性搜索所产生的效率问题，并且我们通过基于角度边界的原型学习来实现一种有效的分类器集成方案，并通过使用优化的图像分区方案提高了精度和推理速度，同时与 Retrieval 方法竞争达到新的最高精度。