TALP-UPC at MediaEval 2014 Placing Task: Combining geographical knowledge bases and language models for large-scale textual georeferencing
Document typeConference report
Rights accessOpen Access
This paper describes our Georeferencing approaches, experiments, and results at the MediaEval 2014 Placing Task evaluation. The task consists of predicting the most probable geographical coordinates of Flickr images and videos using its visual, audio and metadata associated features. Our approaches used only Flickr users textual metadata annotations and tagsets. We used four approaches for this task: 1) an approach based on Geographical Knowledge Bases (GeoKB), 2) the Hiemstra Language Model (HLM) approach with Re-Ranking, 3) a combination of the GeoKB and the HLM (GeoFusion). 4) a combination of the GeoFusion with a HLM model derived from the English Wikipedia georeferenced pages. The HLM approach with Re-Ranking showed the best performance within 10m to 1km distances. The GeoFusion approaches achieved the best results within the margin of errors from 10km to 5000km. This work has been supported by the Spanish Research Department (SKATER Project: TIN2012-38584-C06-01). TALP Research Center is recognized as a Quality Research Group (2014 SGR 1338) by AGAUR, the Research Department of the Catalan Government.
CitationFerrés, D.; Rodríguez, H. TALP-UPC at MediaEval 2014 Placing Task: Combining geographical knowledge bases and language models for large-scale textual georeferencing. A: Multimedia Benchmark Workshop. "Working Notes Proceedings of the MediaEval 2014 Workshop". Barcelona: CEUR-WS.org, 2014, p. 1-2.