Relevance filtering meets active learning
Damian Borth, Adrian Ulges, Thomas Michael Breuel · 2010
We address the challenge of training visual concept detectors on web video as available from portals such as YouTube. In contrast to high-quality but small manually acquired training sets, this setup permits us to scale up concept detection to very large training sets and concept vocabularies. On the downside, web tags are only weak indicators of concept presence, and web video training data contains lots of non-relevant content.