Abstract

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.

Paper

Document PDF (6 Sep 2010) Section 3.1 was updated from the conference version with equation (1) corrected. Bibtex @InProceedings{Knopp10, 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}

Data

	Street-view data.       The set of locations and viewing angles is available upon request. Please email to Jan-DOT-Knopp-AT-esat-DOT-kuleuven-DOT-be.
	Query images.       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.