Abstract
Automatic image tagging is becoming increasingly important to organize large amounts of image data. To identify concepts in images, these tagging systems rely on large sets of annotated image training sets. In this work we analyze image sets taken from online community-driven image databases, such as Flickr, for use in concept identification. Real-world performance is measured using our flexible tagging system, Tagr.
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Renn, M., van Beusekom, J., Keysers, D., Breuel, T.M. (2010). Automatic Image Tagging Using Community-Driven Online Image Databases. In: Detyniecki, M., Leiner, U., Nürnberger, A. (eds) Adaptive Multimedia Retrieval. Identifying, Summarizing, and Recommending Image and Music. AMR 2008. Lecture Notes in Computer Science, vol 5811. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14758-6_10
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DOI: https://doi.org/10.1007/978-3-642-14758-6_10
Publisher Name: Springer, Berlin, Heidelberg
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