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Learning landmarks by exploiting social media
Abstract This paper introduces methods for automatiThis 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.uggestion and content-relevant re-ranking.
Abstractsub This paper introduces methods for automatiThis 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.uggestion and content-relevant re-ranking.
Bibtextype inproceedings  +
Doi 10.1007/978-3-642-11301-7_23  +
Has author Liang C.-K. + , Hsieh Y.-T. + , Chuang T.-J. + , Yafang Wang + , Weng M.-F. + , Chuang Y.-Y. +
Has extra keyword Association analysis + , Automatic annotation + , Content-based + , Indexing methods + , Re-ranking + , Social media + , Spatial distribution + , Visual feature + , Wikipedia + , Indexing (of information) + , Photography + , Ontology +
Isbn 3642113001; 9783642113000  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 207–217  +
Published in Lecture Notes in Computer Science +
Title Learning landmarks by exploiting social media +
Type conference paper  +
Volume 5916 LNCS  +
Year 2009 +
Creation dateThis property is a special property in this wiki. 7 November 2014 23:57:02  +
Categories Publications without keywords parameter  + , Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Conference papers  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 7 November 2014 23:57:02  +
DateThis property is a special property in this wiki. 2009  +
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