Abstract
This paper presents an approach for translation of tags in professional and social image databases, using an original lexical resource extracted from Wikipedia. The translation integrates a tag sense disambiguation algorithm based on WordNet and Wikipedia (as external resources defining word senses). Our disambiguation technique uses the Lesk algorithm, extended gloss overlaps and similarity measures in order to achieve successful resolution of lexical ambiguity and accurate translation of tags. We show how to involve Wikipedia as a source of translation correspondences since open WordNets are not available for most languages. Experimental results and performance evaluation show 97 % accuracy for professional images and 86 % accuracy for social images from Flickr. This translation technique can be applied by auto-tagging programs and information retrieval systems.
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Acknowledgements
This research is partially supported by the EC FP7 grant 316087 AComIn “Advanced Computing for Innovation”, 2012–2016. It is also related to the COST Action IC1307 “Integrating Vision and Language (iV&L Net)”. The authors are thankful to Imagga’s technical team for their comments, recommendations and experimental datasets.
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Kanishcheva, O., Angelova, G., Nikolov, S.G. (2016). Towards Translation of Tags in Large Annotated Image Collections. In: Dichev, C., Agre, G. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2016. Lecture Notes in Computer Science(), vol 9883. Springer, Cham. https://doi.org/10.1007/978-3-319-44748-3_14
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DOI: https://doi.org/10.1007/978-3-319-44748-3_14
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