Proportional Search Space Reduction: Proportional Search Space Reduction: A Novel Metric for Cross-View Image Geo-Location

Identifying where a photo was taken can be achieved by matching the query ground view image to a satellite image of known location. This has been done in the past using Siamese Neural Networks by training a model that embeds images into feature vectors and com-
paring the distance between resultant vectors within the space to find a close match. Historically the number of correct recalls within top k% of matches was used as a metric when testing these models, named Re-call at k (R@k). This paper highlights an issue with prior implementations of the canonical R@k metric related to boundary cases, leading
to the miscounting of recalled images. As a result, models that provide state-of-the-art performance when measured using R@k may yield poor qualitative results in practice. Therefore, this paper proposes a novel metric, Proportional Search Space Reduction (PSSR), which measures how much the search space is reduced by a model under assessment, and
has the potential to include boundary cases that R@k may miss. Three models were trained and evaluated to show that models with high Recall at 1% do not perform as well in real world applications as the metric may suggest, and proposes the use of PSSR for future research into the problem.