Automatic video tagging using content redundancy

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