Learning the trip suggestion from landmark photos on the web

In this paper, we introduce a novel touristic trip suggestion system to facilitate the traveling of mobile users in a given city. Given the current user location and his touristic destination, our system can suggest a shortest trip path that visits as many popular landmarks as possible. To this end, we collect geographical tagged photos from Flickr [1] and Panoramio [2] photo sharing websites. Then a geographical graph is constructed by modeling photos as vertices and their geographical and visual closenesses as connection strengths. In this graph, we mine a dominant subgraph by quantizing nearby and visually duplicated vertices, and then trimming unpopular subgraphs. Such dominant subgraph only retains the popular landmarks from the consensus of travelers in this city. In online suggestion, we map the current user location and the target location to the nearest vertices in this subgraph, based on which an optimal trip is suggested through a shortest path search. We have quantitatively validated our system in typical areas including Beijing and New York City, with quantitative comparisons to alternative approaches.