City-Scale Location Recognition

Grant Schindler, Matthew Brown, Richard Szeliski

Research output: Contribution to conferencePaper

456 Citations (Scopus)

Abstract

We look at the problem of location recognition in a large image dataset using a vocabulary tree. This entails finding the location of a query image in a large dataset containing 3times104 streetside images of a city. We investigate how the traditional invariant feature matching approach falls down as the size of the database grows. In particular we show that by carefully selecting the vocabulary using the most informative features, retrieval performance is significantly improved, allowing us to increase the number of database images by a factor of 10. We also introduce a generalization of the traditional vocabulary tree search algorithm which improves performance by effectively increasing the branching factor of a fixed vocabulary tree.
Original languageEnglish
DOIs
Publication statusPublished - Jun 2007
EventCVPR '07: IEEE Conference on Computer Vision and Pattern Recognition, 2007 - Minneapolis
Duration: 17 Jun 200722 Jun 2007

Conference

ConferenceCVPR '07: IEEE Conference on Computer Vision and Pattern Recognition, 2007
CityMinneapolis
Period17/06/0722/06/07

Cite this

Schindler, G., Brown, M., & Szeliski, R. (2007). City-Scale Location Recognition. Paper presented at CVPR '07: IEEE Conference on Computer Vision and Pattern Recognition, 2007, Minneapolis, . https://doi.org/10.1109/CVPR.2007.383150