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Cited by 626

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

City-Scale Location Recognition

Schindler, Grant, Brown, Matthew, Szeliski, Richard·2007·2007 IEEE Conference on Computer Vision and Pattern Recognition
DOI 10.1109/cvpr.2007.383150
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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 3times104streetside 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.

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Metadata

Authors
Schindler, G., Brown, M., & Szeliski, R.
Venue
2007 IEEE Conference on Computer Vision and Pattern Recognition
Publisher
IEEE
Published
2007-06
Pages
1-7
ISSN
1063-6919 (ISSN-L)
DOI
10.1109/cvpr.2007.383150
OpenAlex
W2134446283
Cited by
626
References
2
FWCI
26.82
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: City-Scale Location Recognition. https://app.cochat.ai/papers/10.1109/cvpr.2007.383150
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Schindler, G., Brown, M., & Szeliski, R. (2007). City-Scale Location Recognition. 2007 IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2007.383150

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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