Learning Landmarks by Exploiting Social Media

  • Chia-Kai Liang
  • Yu-Ting Hsieh
  • Tien-Jung Chuang
  • Yin Wang
  • Ming-Fang Weng
  • Yung-Yu Chuang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5916)


This paper introduces methods for automatic annotation of landmark photographs via learning textual tags and visual features of landmarks from landmark photographs that are appropriately location-tagged from social media. By analyzing spatial distributions of text tags from Flickr’s geotagged photos, we identify thousands of tags that likely refer to landmarks. Further verification by utilizing Wikipedia articles filters out non-landmark tags. Association analysis is used to find the containment relationship between landmark tags and other geographic names, thus forming a geographic hierarchy. Photographs relevant to each landmark tag were retrieved from Flickr and distinctive visual features were extracted from them. The results form ontology for landmarks, including their names, equivalent names, geographic hierarchy, and visual features. We also propose an efficient indexing method for content-based landmark search. The resultant ontology could be used in tag suggestion and content-relevant re-ranking.


Visual Word Query Image Place Semantic Object Retrieval Photo Collection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Chia-Kai Liang
    • 1
  • Yu-Ting Hsieh
    • 1
  • Tien-Jung Chuang
    • 1
  • Yin Wang
    • 1
  • Ming-Fang Weng
    • 1
  • Yung-Yu Chuang
    • 1
  1. 1.National Taiwan University 

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