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Automatic Image Tagging Using Community-Driven Online Image Databases

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 5811)

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.

Keywords

  • Query Image
  • Word Number
  • Fire Server
  • Query Tree
  • Annotate Image

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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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

  • Print ISBN: 978-3-642-14757-9

  • Online ISBN: 978-3-642-14758-6

  • eBook Packages: Computer ScienceComputer Science (R0)