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Knowledge-driven reverse geo-tagging for annotated images

Abstract : Currently, Reverse Geo-tagging relies on the keywords describing an image and use probabilistic algorithms to guess the localization of the depicted scene. However, such algorithms still perform poorly and show clear limitations. Notably, the location estimation only occurs at the landmark level; regions or countries are only processed through their centroid. In this paper, we address this particular issue by exploring a semantic approach, which identifies geographical entities among the keywords to localize the picture (being a landmark or a country). We leverage components of the Linked Open Data cloud to find possible entities. The benefits of our approach, as opposed to numerical approaches, include an in-depth study of the ``geo-relevance'' of an image
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Contributor : Elöd Egyed-Zsigmond <>
Submitted on : Friday, July 8, 2016 - 11:55:40 AM
Last modification on : Tuesday, June 1, 2021 - 2:08:07 PM



Victor Charpenay, Elod Egyed-Zsigmond, Harald Kosch. Knowledge-driven reverse geo-tagging for annotated images. Document Numérique, Lavoisier, 2016, Une vision SI de l'ingénieirie des documents, 19, pp.83-102. ⟨10.3166/DN.19.1.83-102⟩. ⟨hal-01343362⟩



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