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

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

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.