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How Spatial Segmentation improves the Multimodal Geo-Tagging
Citation key 1387Kelm2012
Author Pascal Kelm and Sebastian Schmiedeke and Thomas Sikora
Title of Book Working Notes Proceedings of the MediaEval 2012 Workshop
Pages 9–10
Year 2012
Address Santa Croce in Fossabanda Piazza Santa Croce, 5 - 56125 - Pisa - Toscana - Italia
Month oct
Note ISSN 1613-0073
Editor Martha A. Larson, Sebastian Schmiedeke, Pascal Kelm, Adam Rae, Vasileios Mezaris, Tomas Piatrik, Mohammad Soleymani, Florian Metze, Gareth J.F. Jones
Publisher CEUR-WS.org
Abstract In this paper we present a hierarchical, multi-modal ap- proach in combination with different granularity levels for the Placing Task at the MediaEval benchmark 2012. Our approach makes use of external resources like gazetteers to extract toponyms in the metadata and of visual and textual features to identify similar content. First, the bounderies detection recognizes the country and its dimension to speed up the estimation and to eliminate geographical ambiguity. Next, we prepared a training database to group them to- gether into geographical regions and to build a hierarchical model. The fusion of visual and textual methods for differ- ent granularities is used to classify the videos’ location into possible regions. At the end the Flickr videos are tagged with the geo-information of the most similar training image within the regions that is previously filtered by the proba- bilistic model for each test video.
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