||Wohlhart Paul, Köstinger Martin, Roth Peter M., Bischof Horst
||In Proceedings 35th OAGM/AAPR Workshop, Graz, Austria
||Videos are often associated with additional information that could be valuable for interpretation of its content. This especially applies for the recognition of faces within video streams, where often cues such as transcripts and subtitles are available. However, this data is not completely reliable and might be ambiguously labeled. To overcome these limitations, we propose a new semi supervised multiple instance learning algorithm, where the contribution is twofold. First, we can transfer information on labeled bags of instances, thus, enabling us to weaken the prerequisite knowing each label for each instance. Second, we can integrate unlabeled data, given only probabilistic information in form of priors. The benefits of the approach are demonstrated for face recognition in videos on a publicly available benchmark dataset.