Learned lexicon-driven interactive video retrieval

Authors:
Cees Snoek;Marcel Worring;Dennis Koelma;Arnold Smeulders
Affiliations:
Intelligent Systems Lab Amsterdam, University of Amsterdam, Amsterdam, The Netherlands;Intelligent Systems Lab Amsterdam, University of Amsterdam, Amsterdam, The Netherlands;Intelligent Systems Lab Amsterdam, University of Amsterdam, Amsterdam, The Netherlands;Intelligent Systems Lab Amsterdam, University of Amsterdam, Amsterdam, The Netherlands
Venue:
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
Year:
2006

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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.