Automatic Video Tagging Using Content Redundancy

Abstract

The analysis of the leading social video sharing platform YouTube reveals a high amount of redundancy, in the form of videos with overlapping or duplicated content. In this pa-per, we show that this redundancy can provide useful infor-mation about connections between videos. We reveal these links using robust content-based video analysis techniques and exploit them for generating new tag assignments. To this end, we propose different tag propagation methods for automatically obtaining richer video annotations. Our tech-niques provide the user with additional information about videos, and lead to enhanced feature representations for ap-plications such as automatic data organization and search. Experiments on video clustering and classification as well as a user evaluation demonstrate the viability of our approach