City Scale Image Geolocalization via Dense Scene Alignment
Abstract
Predicting where a photo was taken is quite important and yet a challenging task for computer vision algorithms. Our motivation is to solve this difficult problem in a city scale setting by employing a data-driven approach. In order to pursue this goal, we developed a fast and robust scene matching method that follows a coarse-to-fine strategy. In particular, we combine scene retrieval via global features and dense scene alignment and use a large set of geo-tagged images of downtown San Francisco in our evaluation. The experimental results show that the proposed approach, despite its simplicity, is surprisingly effective and achieves comparable results with the state-of-the-art.
Cite
Text
Yagcioglu et al. "City Scale Image Geolocalization via Dense Scene Alignment." IEEE/CVF Winter Conference on Applications of Computer Vision, 2015. doi:10.1109/WACV.2015.102Markdown
[Yagcioglu et al. "City Scale Image Geolocalization via Dense Scene Alignment." IEEE/CVF Winter Conference on Applications of Computer Vision, 2015.](https://mlanthology.org/wacv/2015/yagcioglu2015wacv-city/) doi:10.1109/WACV.2015.102BibTeX
@inproceedings{yagcioglu2015wacv-city,
title = {{City Scale Image Geolocalization via Dense Scene Alignment}},
author = {Yagcioglu, Semih and Erdem, Erkut and Erdem, Aykut},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
year = {2015},
pages = {726-732},
doi = {10.1109/WACV.2015.102},
url = {https://mlanthology.org/wacv/2015/yagcioglu2015wacv-city/}
}