@Article{Karydis_2014,
  author        = {Karydis, Ioannis and Avlonitis, Markos and Chorianopoulos, Konstantinos and Sioutas, Spyros},
  title         = {{Identifying Important Segments in Videos: A Collective Intelligence Approach}},
  journal       = {International Journal on Artificial Intelligence Tools},
  year          = {2014},
  volume        = {23},
  number        = {02},
  month         = {apr},
  abstract      = {This work studies collective intelligence behavior of Web users that share and watch video content.
Accordingly, it is proposed that the aggregated users' video activity exhibits characteristic patterns. Such patterns may be
used in order to infer important video scenes leading thus to collective intelligence concerning the video content. To this
end, experimentation is based on users' interactions (e.g., pause, seek/scrub) that have been gathered in a controlled user
experiment with information-rich videos. Collective information seeking behavior is then modeled by means of the corresponding
probability distribution function. Thus, it is argued that the bell-shaped reference patterns are shown to significantly
correlate with predefined scenes of interest for each video, as annotated by the users. In this way, the observed collective
intelligence may be used to provide a video-segment detection tool that identifies the importance of video scenes. Accordingly,
both a stochastic and a pattern matching appro...},
  doi           = {10.1142/S0218213014400107},
  url_Paper={Karydis_2014.pdf},
  issn          = {0218-2130},
  keywords      = {Data minining,Signal processing,Video,important-segment detection,interaction,semantics,signal
processing,user activity,user-based,web},
  language      = {en},
  mendeley-tags = {Data minining,Signal processing},
  publisher     = {World Scientific Publishing Company},
  url           = {http://www.worldscientific.com/doi/abs/10.1142/S0218213014400107},
}

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Accordingly, it is proposed that the aggregated users' video activity exhibits characteristic patterns. Such patterns may be used in order to infer important video scenes leading thus to collective intelligence concerning the video content. To this end, experimentation is based on users' interactions (e.g., pause, seek/scrub) that have been gathered in a controlled user experiment with information-rich videos. Collective information seeking behavior is then modeled by means of the corresponding probability distribution function. Thus, it is argued that the bell-shaped reference patterns are shown to significantly correlate with predefined scenes of interest for each video, as annotated by the users. In this way, the observed collective intelligence may be used to provide a video-segment detection tool that identifies the importance of video scenes. 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