TubeFiler
Damian Borth, J.J. van Hees, Markus Koch, Adrian Ulges, CHRISTIAN HANSJOACHIM SCHULZE, Thomas Michael Breuel, Roberto Paredes · 2009
While hierarchies are powerful tools for organizing content in other application areas, current web video platforms offer only limited support for a taxonomy-based browsing. To overcome this limitation, we present a framework called TubeFiler. Its two key features are an automatic multimodal categorization of videos into a genre hierarchy, and a support of additional fine-grained hierarchy levels based on unsupervised learning. We present experimental results on real-world YouTube clips with a 2-level 46-category genre hierarchy, indicating that - though the problem is clearly challenging - good category suggestions can be achieved. For example, if TubeFiler suggests 5 categories, it hits the right one (or at least its supercategory) in 91.8% of cases.