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CIVR
2006
Springer

Learned Lexicon-Driven Interactive Video Retrieval

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Learned Lexicon-Driven Interactive Video Retrieval
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-bykeyword, 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.
Cees Snoek, Marcel Worring, Dennis Koelma, Arnold
Added 20 Aug 2010
Updated 20 Aug 2010
Type Conference
Year 2006
Where CIVR
Authors Cees Snoek, Marcel Worring, Dennis Koelma, Arnold W. M. Smeulders
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