Improving Automatic Video Retrieval with Semantic Concept Detection

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. Tire experiments use tire 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 tire individual detectors is still often quite modest.