It is often the case that only sparse sequences of videos from scientific underwater surveys actually contain important information for the expert. A system automatically detecting those critical parts, particularly during the post-mission tape analysis, would alleviate the expert work load and improve data exploitation. In this paper, we present a novel set of algorithms to detect in real time significant context changes in benthic videos. The detectors presented rely on an unsupervised image classification scheme: the time changes in the image contents are analyzed in the feature space. The algorithms are explained in detail, and experimental results with real underwater images reported. Various issues related to the complexity of the problem of automatically analysing underwater videos are also discussed.