  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.
