  We propose an image representation and matching approach that substantially
improves visual-based location estimation for images. The main novelty of the
approach, called distinctive visual element matching (DVEM), is its use of
representations that are specific to the query image whose location is being
predicted. These representations are based on visual element clouds, which
robustly capture the connection between the query and visual evidence from
candidate locations. We then maximize the influence of visual elements that are
geo-distinctive because they do not occur in images taken at many other
locations. We carry out experiments and analysis for both geo-constrained and
geo-unconstrained location estimation cases using two large-scale,
publicly-available datasets: the San Francisco Landmark dataset with $1.06$
million street-view images and the MediaEval '15 Placing Task dataset with
$5.6$ million geo-tagged images from Flickr. We present examples that
illustrate the highly-transparent mechanics of the approach, which are based on
common sense observations about the visual patterns in image collections. Our
results show that the proposed method delivers a considerable performance
improvement compared to the state of the art.
