Abstract

We seek to recognize the place depicted in a query image using a database of “street side” images annotated with geolocation in- formation. This is a challenging task due to changes in scale, viewpoint and lighting between the query and the images in the database. One of the key problems in place recognition is the presence of ob jects such as trees or road markings, which frequently occur in the database and hence cause significant confusion between different places. As the main contri- bution, we show how to avoid features leading to confusion of particular places by using geotags attached to database images as a form of supervi- sion. 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. We show the method combines well with the state of the art bag-of-features model including query expan- sion, and demonstrate place recognition that generalizes over wide range of viewpoints and lighting conditions. Results are shown on a geotagged database of over 17K images of Paris downloaded from Google Street View.