We study the usefulness of intermediate semantic concepts in bridging the semantic gap in automatic video retrieval. The results of a series of large-scale retrieval experiments, which combine text-based search, content-based retrieval, and concept-based retrieval, is presented. The experiments use the common video data and sets of queries from three successive TRECVID evaluations. By including concept detectors, we observe a consistent improvement on the search performance, despite the fact that the performance of the individual detectors is still often quite modest. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Koskela, M., Sjöberg, M., & Laaksonen, J. (2009). Improving automatic video retrieval with semantic concept detection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5575 LNCS, pp. 480–489). https://doi.org/10.1007/978-3-642-02230-2_49
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