The automatic detection of novelty, or newness, as part of an information retrieval system would greatly improve a searcher’s experience by presenting “documents” in order of how much extra information they add to what is already known instead of how similar they are to a user’s query. In this paper we present a novelty detection system evaluated on the AQUAINT text collection as part of our TREC 2004 Novelty Track experiments. Subsequent to participation in TREC, the algorithm has been evaluated on another collection with its parameters optimized and we present those results here. We also discuss how we are extending the text-only approach to novelty detection to also include input from video analysis.