We seek to recognize the place depicted in a query image
using a database of “street side” images annotated with
geolocation information. This is a challenging task due to
changes in scale, viewpoint and lighting between the query
and the images in the database. The image database may
also contain objects, such as trees or road markings, which
frequently occur and hence can cause significant confusion
between different places. We employ the efficient bag-offeatures
representation previously used for object retrieval
in large image collections. As the main contribution, we
show how to avoid features leading to confusion of particular
places by using geotags attached to database images as
a form of supervision. We develop a method for automatic
detection of image-specific and spatially-localized groups
of confusing features, and demonstrate that suppressing
them significantly improves place recognition performance
while reducing the database size. As a second contribution,
we demonstrate that enhancing street side imagery
with images downloaded from community photo-collections
can lead to improved place recognition performance. Results
are shown on a geotagged database of over 17K images
of Paris downloaded from Google Street View.
(6 Sep 2010)
Section 3.1 was updated from the conference version with equation (1) corrected.
author = "Knopp, J. and Sivic, J. and Pajdla, T.",
title = "Avoiding confusing features in place recognition",
booktitle = "Proceedings of the European Conference
on Computer Vision",
year = 2010}
The set of locations and viewing angles is available upon request. Please email to Jan-DOT-Knopp-AT-esat-DOT-kuleuven-DOT-be.
Test set of 200 query test images from Panoramio is available upon request. Please email to Jan-DOT-Knopp-AT-esat-DOT-kuleuven-DOT-be.