Proceedings-Artikel

TubeFiler - an Automatic Web Video Categorizer

Damian Borth; Jörn Hees; Markus Koch; Adrian Ulges; Christian Schulze; Thomas Breuel; Roberto Paredes
In: ACM (Hrsg.). ACM Mutimedia. ACM International Conference on Multimedia (ACM MM-09), October 19-24, Beijing, China, ACM, 10/2009.

Abstract

While current web video platforms offer only limited support for a taxonomy-based browsing, hierarchies are powerful tools for organizing content in other application areas. To overcome this limitation, we present a framework called TubeFiler. Its two key features are an automatic multi-modal 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 its supercategory) in 91.8% of cases.

Projekte

MOONVID

Weitere Links

BibTeX

http://madm.dfki.de/demo/tubefiler/

google_challenge-tubefiler.pdf