Learned Lexicon-Driven Interactive Video Retrieval C. G. M. Snoek, M. Worring, D. C. Koelma, A. W. M. Smeulders In International Conference on Image and Video Retrieval 2006. [bibtex] [pdf] [url]

We combine in this paper automatic learning of a large lexicon of semantic concepts with traditional video retrieval methods into a novel approach to narrow the semantic gap. The core of the proposed solution is formed by the automatic detection of an unprecedented lexicon of 101 concepts. From there, we explore the combination of query-by-concept, query-by-example, query-by-keyword, and user interaction into the \emph{MediaMill} semantic video search engine. We evaluate the search engine against the 2005 NIST TRECVID video retrieval benchmark, using an international broadcast news archive of 85 hours. Top ranking results show that the lexicon-driven search engine is highly effective for interactive video retrieval.

@InProceedings{SnoekICIVR2006,
  author       = "Snoek, C. G. M. and Worring, M. and Koelma, D. C. and Smeulders, A. W. M.",
  title        = "Learned Lexicon-Driven Interactive Video Retrieval",
  booktitle    = "International Conference on Image and Video Retrieval",
  volume       = "LNCS 4071",
  pages        = "11--20",
  year         = "2006",
  editor       = "Sundaram, H. et al.",
  url          = "https://ivi.fnwi.uva.nl/isis/publications/2006/SnoekICIVR2006",
  pdf          = "https://ivi.fnwi.uva.nl/isis/publications/2006/SnoekICIVR2006/SnoekICIVR2006.pdf",
  has_image    = 1
}