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. 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.

• Type: Conference
• Year: 2009
• Where: SCIA
• Authors: Markus Koskela, Mats Sjöberg, Jorma Laaksonen
• Keywords: Automatic Video Retrieval, Concept-based Retrieval, Image, Intermediate Semantic Concepts, SCIA 2009