Leveraging Knowledge from the Linked Open Data Cloud in the task of Reverse Geo-tagging
Résumé
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
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.