Crowdsourcing Visual Detectors for Video Search

In this paper we study social tagging at the video fragment-level using a combination of automated content understanding and the wisdom of the crowds. We are interested in the question whether crowdsourcing can be beneficial to a video search engine that automatically recognizes video fragments on a semantic level. To answer this question, we perform a 3-month online field study with a concert video search engine targeted at a dedicated user community of pop concert enthusiasts. We harvest the feedback of more than 500 active users and perform two experiments. In experiment 1 we measure user incentive to provide feedback, in experiment 2 we study the tradeoff between feedback quality and quantity when aggregated over multiple users. Results show that users provide sufficient feedback, which becomes highly reliable when a crowd agreement of 67% is enforced.

@InProceedings{FreiburgICM2011,
  author       = "Freiburg, B. and Kamps, J. and Snoek, C. G. M.",
  title        = "Crowdsourcing Visual Detectors for Video Search",
  booktitle    = "ACM International Conference on Multimedia",
  pages        = "913--916",
  year         = "2011",
  url          = "http://www.science.uva.nl/research/publications/2011/FreiburgICM2011"
}