# Knowledge-driven reverse geo-tagging for annotated images

1 DRIM - Distribution, Recherche d'Information et Mobilité
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
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
Keywords :
Document type :
Journal articles
Domain :

https://hal.archives-ouvertes.fr/hal-01343362
Contributor : Elöd Egyed-Zsigmond <>
Submitted on : Friday, July 8, 2016 - 11:55:40 AM
Last modification on : Thursday, February 7, 2019 - 5:17:36 PM

### Citation

Victor Charpenay, Elod Egyed-Zsigmond, Harald Kosch. Knowledge-driven reverse geo-tagging for annotated images. Revue des Sciences et Technologies de l'Information - Série 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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