Image-to-GPS Verification Through A Bottom-Up Pattern Matching Network
This addresses the problem of verifying image locations for applications like geolocation or security, but it is incremental as it builds on existing image verification approaches.
The paper tackles the image-to-GPS verification problem by treating it as an image verification task between a query image and a reference image from a claimed GPS location, proposing a bottom-up pattern matching network that outperforms state-of-the-art methods in verification and localization.
The image-to-GPS verification problem asks whether a given image is taken at a claimed GPS location. In this paper, we treat it as an image verification problem -- whether a query image is taken at the same place as a reference image retrieved at the claimed GPS location. We make three major contributions: 1) we propose a novel custom bottom-up pattern matching (BUPM) deep neural network solution; 2) we demonstrate that the verification can be directly done by cross-checking a perspective-looking query image and a panorama reference image, and 3) we collect and clean a dataset of 30K pairs query and reference. Our experimental results show that the proposed BUPM solution outperforms the state-of-the-art solutions in terms of both verification and localization.