For large scale automatic semantic video characterization, it is necessary to learn and model a large number of semantic concepts. A major obstacle to this is the insufficiency of labeled training samples. Semi-supervised learning algorithms such as co-training may help by incorporating a large amount of unlabeled data, which allows the redundant information across views to improve the learning performance. Although co-training has been successfully applied in several domains, it has not been used to detect video concepts before. In this paper, we extend co-training to the domain of video concept detection and investigate different strategies of co-training as well as their effects to the detection accuracy. We demonstrate performance based on the guideline of the TRECVID' 03 semantic concept extraction task.