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.
CITATION STYLE
Cheng, J., Wu, Y., Abd-Almageed, W., & Natarajan, P. (2019). Image-to-GPS Verification Through a Bottom-Up Pattern Matching Network. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11365 LNCS, pp. 546–561). Springer Verlag. https://doi.org/10.1007/978-3-030-20873-8_35
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