Learning Selection of User Generated Event Videos

Abstract

User generated images and videos can enhance the coverage of live events on social and online media, as well as in broadcasts. However, the quality, relevance and complementarity of the received contributions varies greatly. In a live scenario, it is often not feasible for the editorial team to review all content and make selections. We propose to support this work by automatic selection based on captured metadata, and extracted quality and content features. It is usually desired to have a human in the loop, thus the automatic system does not make a final decision, but provides a ranked list of content items. As the operator makes selections, the automatic system shall learn from these decisions, which may change over time. Due to the need for online learning and quick adaptation, we propose the use of online random forests for this task. We show on data from three real live events that the approach is able to provide a ranking based on the predicted selection likelihood after an initial adjustment phase.