Automatic concept-to-query mapping for web-based concept detector training

  • Authors:

  • Damian Borth;Adrian Ulges;Thomas Michael Breuel

  • Affiliations:

  • University of Kaiserslautern, Kaiserslautern, Germany;German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany;University of Kaiserslautern, Kaiserslautern, Germany

  • Venue:
  • MM '11 Proceedings of the 19th ACM international conference on Multimedia
  • Year:
  • 2011

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Abstract

Nowadays, online platforms like YouTube provide massive content for training of visual concept detectors. However, it remains a difficult challenge to retrieve the right training content from such platforms since the underlying query construction can be arbitrarily complex. In this paper we present an approach, which offers an automatic concept-to-query mapping for training data acquisition from such platforms. Queries are automatically constructed by a keyword selection and a category assignment using ImageNet and Google Sets as external sources. Our results demonstrate that the proposed method is able to reach retrieval results comparable to queries constructed by humans providing 76% more relevant content for detector training than a one-to-one mapping of concept names to retrieval queries would do.