The automated indexing of image and video is a difficult problem because of the "distance" between the arrays of numbers encoding these documents and the concepts (e.g. people, places, events or objects) with which we wish to annotate them. Methods exist for this but their results are far from satisfactory in terms of generality and accuracy. Existing methods typically use a single set of such examples and consider it as uniform. This is not optimal because the same concept may appear in various contexts and its appearance may be very different depending upon these contexts. The context has been widely used in the state of the art to treat various problems. However, the temporal context seems to be the most crucial and the most effective for the case of videos. In this paper, we present a comparative study between two methods exploiting the temporal context for semantic video indexing. The proposed approaches use temporal information that is derived from two different sources: low-level content and semantic information. Our experiments on TRECVID'12 collection showed interesting results that confirm the usefulness of the temporal context and demonstrate which of the two approaches is more effective.