Key points are not available for this paper at this time.
Can a machine tell us if an image was taken in Beijing or New York? Automated identification of the geographical coordinates based on image content is of particular importance to data mining systems, because geolocation provides a large source of context for other useful features of an image. However, successful localization of unannotated images requires a large collection of images that cover all possible locations. Brute-force searches over the entire databases are costly in terms of computation and storage requirements, and achieve limited results. Knowing what visual features make a particular location unique or similar to other locations can be used for choosing a better match between spatially distance locations. However, doing this at global scales is a challenging problem. In this paper we propose an on-line, unsupervised, clustering algorithm called Location Aware Self-Organizing Map (LASOM), for learning the similarity graph between different regions. The goal of LASOM is to select key features in specific locations so as to increase the accuracy in geotagging untagged images, while also reducing computational and storage requirements. Different from other Self-Organizing Map algorithms, LASOM provides the means to learn a conditional distribution of visual features, conditioned on geospatial coordinates. We demonstrate that the generated map not only preserves important visual information, but provides additional context in the form of visual similarity relationships between different geographical areas. We show how this information can be used to improve geotagging results when using large databases.
Kit et al. (Tue,) studied this question.