Learning landmarks by exploiting social media
|Learning landmarks by exploiting social media|
|Author(s)||Liang C.-K., Hsieh Y.-T., Chuang T.-J., Wang Y., Weng M.-F., Chuang Y.-Y.|
|Published in||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Keyword(s)||Unknown (Extra: Association analysis, Automatic annotation, Content-based, Indexing methods, Re-ranking, Social media, Spatial distribution, Visual feature, Wikipedia, Indexing (of information), Photography, Ontology)|
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Learning landmarks by exploiting social media is a 2009 conference paper written in English by Liang C.-K., Hsieh Y.-T., Chuang T.-J., Wang Y., Weng M.-F., Chuang Y.-Y. and published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
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.
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