Automatic propagation of manual annotations for multimodal person identification in TV shows

In this paper an approach to human annotation propagation for person identification in the multimodal context is proposed. A system is used, which combines speaker diarization and face clustering to produce multimodal clusters. The whole multimodal clusters are later annotated rather than just single tracks, which is done by propagation. Optical character recognition systems provides initial annotation. Four different strategies, which select candidates for annotation, are tested. The initial results of annotation propagation are promising. With the use of a proper active learning selection strategy the human annotator involvement could be reduced even further.