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 MIST 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. © Springer-Verlag Berlin Heidelberg 2006.
Cite
CITATION STYLE
Snoek, C., Worring, M., Koelma, D., & Smeulders, A. (2006). Learned lexicon-driven interactive video retrieval. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4071 LNCS, pp. 11–20). Springer Verlag. https://doi.org/10.1007/11788034_2
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