Semantic indexing and retrieval of multimedia content requires that the content is sufficiently annotated. However, the great volumes of multimedia data and diversity of labels make annotation a difficult and costly process. Disclosed is an annotation framework in which supervised training with partially labeled data is facilitated using active learning. The system trains a classifier with a small set of labeled data and subsequently updates the classifier by selecting a subset of the available data-set according to optimization criteria. The process results in propagation of labels to unlabeled data and greatly facilitates the user in annotating large amounts of multimedia content.