Predicting Good Features for Image Geo-Localization Using Per-Bundle VLAD

Hyo Jin Kim, Enrique Dunn, Jan-Michael Frahm; The IEEE International Conference on Computer Vision (ICCV), 2015, pp. 1170-1178

Abstract


We address the problem of recognizing a place depicted in a query image by using a large database of geo-tagged images at a city-scale. In particular, we discover features that are useful for recognizing a place in a data-driven manner, and use this knowledge to predict useful features in a query image prior to the geo-localization process. This allows us to achieve better performance while reducing the number of features. Also, for both learning to predict features and retrieving geo-tagged images from the database, we propose per-bundle vector of locally aggregated descriptors (PBVLAD), where each maximally stable region is described by a vector of locally aggregated descriptors (VLAD) on multiple scale-invariant features detected within the region. Experimental results show the proposed approach achieves a significant improvement over other baseline methods.

Related Material


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[bibtex]
@InProceedings{Kim_2015_ICCV,
author = {Jin Kim, Hyo and Dunn, Enrique and Frahm, Jan-Michael},
title = {Predicting Good Features for Image Geo-Localization Using Per-Bundle VLAD},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2015}
}