TY - GEN
T1 - Predicting good features for image geo-localization using per-bundle VLAD
AU - Kim, Hyo Jin
AU - Dunn, Enrique
AU - Frahm, Jan Michael
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/2/17
Y1 - 2015/2/17
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84973879896&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84973879896&partnerID=8YFLogxK
U2 - 10.1109/ICCV.2015.139
DO - 10.1109/ICCV.2015.139
M3 - Conference contribution
AN - SCOPUS:84973879896
T3 - Proceedings of the IEEE International Conference on Computer Vision
SP - 1170
EP - 1178
BT - 2015 International Conference on Computer Vision, ICCV 2015
T2 - 15th IEEE International Conference on Computer Vision, ICCV 2015
Y2 - 11 December 2015 through 18 December 2015
ER -