Multimedia Event-Based Video Indexing Using Time Intervals C. G. M. Snoek, M. Worring In IEEE Transactions on Multimedia 2005. [bibtex] [pdf] [url]

We propose the Time Interval Multimedia Event (TIME) framework as a robust approach for classification of semantic events in multimodal video documents. The representation used in TIME extends the Allen time relations and allows for proper inclusion of context and synchronization of the heterogeneous information sources involved in multimodal video analysis. To demonstrate the viability of our approach, it was evaluated on the domains of soccer and news broadcasts. For automatic classification of semantic events, we compare three different machine learning techniques, i.c. C4.5 decision tree, Maximum Entropy, and Support Vector Machine. The results show that semantic video indexing results significantly benefit from using the TIME framework.

@Article{SnoekITM2005,
  author       = "Snoek, C. G. M. and Worring, M.",
  title        = "Multimedia Event-Based Video Indexing Using Time Intervals",
  journal      = "IEEE Transactions on Multimedia",
  number       = "4",
  volume       = "7",
  pages        = "638--647",
  year         = "2005",
  url          = "https://ivi.fnwi.uva.nl/isis/publications/2005/SnoekITM2005",
  pdf          = "https://ivi.fnwi.uva.nl/isis/publications/2005/SnoekITM2005/SnoekITM2005.pdf",
  has_image    = 1
}