Abstract
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.
Supported by the Academy of Finland in the Finnish Centre of Excellence in Adaptive Informatics Research project and by the TKK MIDE programme project UI-ART.
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Koskela, M., Sjöberg, M., Laaksonen, J. (2009). Improving Automatic Video Retrieval with Semantic Concept Detection. In: Salberg, AB., Hardeberg, J.Y., Jenssen, R. (eds) Image Analysis. SCIA 2009. Lecture Notes in Computer Science, vol 5575. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02230-2_49
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DOI: https://doi.org/10.1007/978-3-642-02230-2_49
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