Borth, D., J. Hees, M. Koch, A. Ulges, C. Schulze, T. Breuel, and R. Paredes.
TubeFiler: An Automatic Web Video Categorizer.
In Proceedings of the Seventeen ACM International Conference on Multimedia, Beijing, China, 19-24 October 2009.
Abstract:
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.
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