Visual Concept Learning from Weakly Labeled Web Videos

Concept detection is a core component of video database search, concerned with the automatic recognition of visually diverse categories of objects (“airplane”), locations (“desert”), or activities (“interview”). The task poses a difficult challenge as the amount of accurately labeled data available for supervised training is limited and coverage of concept classes is poor. In order to overcome these problems, we describe the use of videos found on the web as training data for concept detectors, using tagging and folksonomies as annotation sources. This permits us to scale up training to very large data sets and concept vocabularies.