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Semantic concept detection from news videos with self-organizing maps AbstractIn this paper, we consider the automatic identification of video shots that are relevant to a given semantic concept from large video databases. We apply a method of representing semantic concepts as class models on a set of parallel Self-Organizing Maps trained with multimodal low-level features. The presented experiments were conducted using a set of 170 hours of video containing recorded television news programs.
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