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Learned Lexicon-Driven Interactive Video Retrieval

  • Cees Snoek
  • Marcel Worring
  • Dennis Koelma
  • Arnold Smeulders
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4071)

Abstract

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 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.

Keywords

Search Engine Average Precision Semantic Concept Video Retrieval Query Interface 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Cees Snoek
    • 1
  • Marcel Worring
    • 1
  • Dennis Koelma
    • 1
  • Arnold Smeulders
    • 1
  1. 1.Intelligent Systems Lab AmsterdamUniversity of AmsterdamAmsterdamThe Netherlands

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