Semi-automatic soft collaborative annotation for semantic video indexing

The paper proposes a novel semi-automatic soft collaborative annotation scheme for video semantic indexing. To annotate video data effectively and accurately, a video collaborative soft annotation within users´ judgment modeling is first proposed in this paper. We, then, introduce a semiautomatic annotation strategy which combines the active learning and self-training in order to reduce the annotators´ effort. Experiments conducted in TRECVID benchmark show that the proposed approach significantly improves the performance of video annotation.