Global-Scale Location Prediction for Social Images Using Geo-Visual Ranking

We propose an automatic method that addresses the challenge of predicting the geo-location of social images using only the visual content of those images. Our method is able to generate a geo-location prediction for an image globally . In this respect, it contrasts with other existing approaches, specifically with those that generate predictions restricted to specific cities, landmarks, or an otherwise pre-defined set of locations. The essence and the main novelty of our ranking-based method is that for a given query image a geo-location is recommended based on the evidence collected from images that are not only geographically close to this geo-location, but also have sufficient visual similarity to the query image within the considered image collection. Our method is evaluated experimentally on a public dataset of 8.8 million geo-tagged images from Flickr, released by the MediaEval 2013 evaluation benchmark. Experiments show that the proposed method delivers a substantial performance improvement compared to the existing related approaches, particularly for queries with high numbers of neighbors . In addition, a detailed analysis of the method´s performance reveals the impact of different visual feature extraction and image matching strategies, as well as the densities and types of images found at different locations, on the prediction accuracy.