2 research outputs found
Image-to-GPS Verification Through A Bottom-Up Pattern Matching Network
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
QATM: Quality-Aware Template Matching For Deep Learning
Finding a template in a search image is one of the core problems many
computer vision, such as semantic image semantic, image-to-GPS verification
\etc. We propose a novel quality-aware template matching method, QATM, which is
not only used as a standalone template matching algorithm, but also a trainable
layer that can be easily embedded into any deep neural network. Specifically,
we assess the quality of a matching pair using soft-ranking among all matching
pairs, and thus different matching scenarios such as 1-to-1, 1-to-many, and
many-to-many will be all reflected to different values. Our extensive
evaluation on classic template matching benchmarks and deep learning tasks
demonstrate the effectiveness of QATM. It not only outperforms state-of-the-art
template matching methods when used alone, but also largely improves existing
deep network solutions.Comment: Accepted as CVPR 2019 paper. Camera ready versio