Divide&Classify: Fine-Grained Classification for City-Wide Visual Place Recognition

• URL: http://arxiv.org/abs/2307.08417v2
• Date: Wed, 6 Dec 2023 23:55:01 GMT
• Title: Divide&Classify: Fine-Grained Classification for City-Wide Visual Place Recognition
• Authors: Gabriele Trivigno, Gabriele Berton, Juan Aragon, Barbara Caputo, Carlo Masone
• Abstract summary: 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. We show that D&C can be paired with existing retrieval pipelines to speed up computations by over 20 times while increasing their recall.
• Score: 21.039399444257807
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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