Mining tourist routes from Flickr photos

Popular social networking sites like Flickr are nowadays overwhelmed by geo-tagged photos. Semi-automatic discovery of touristic routes and landmarks from such a pool of photos forms a challenging task. In this paper we attempt to analyze user-generated routes within downtown city areas defined around a pre-selected geographical bounding box and derived from a large geo-tagged Flickr dataset, by utilizing a novel two-level clustering scheme. Our goal is to select routes of touristic interest for a given area. Without loss of generality the latter is considered to be a predefined "window" around a city's most famous landmarks and touristic attractions. The herein proposed framework has been applied to a real-life geo-tagged Flickr photos dataset from a major European metropolis: Athens, Greece.