In order to achieve true content-based information retrieval on video we should analyse and index video with \udhigh-level semantic concepts in addition to using user-generated tags and structured metadata like title, date, \udetc. However the range of such high-level semantic concepts, detected either manually or automatically, \udusually limited compared to the richness of information content in video and the potential vocabulary of \udavailable concepts for indexing. Even though there is work to improve the performance of individual concept \udclassifiers, we should strive to make the best use of whatever partial sets of semantic concept occurrences \udare available to us. We describe in this paper our method for using association rule mining to automatically \udenrich the representation of video content through a set of semantic concepts based on concept co-occurrence \udpatterns. We describe our experiments on the TRECVid 2005 video corpus annotated with the 449 concepts \udof the LSCOM ontology. The evaluation of our results shows the usefulness of our approach.
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