TubeFiler
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Welcome to TubeFiler Webdemo
TubeFiler - an Automatic Web Video Catgorizer In context of the Multimedia Grand Challenge, a set of open problems specified by industrial partners, we present the TubeFiler framework, as our contribution to the Google Challenge: Robust, As-Accurate-As-Human Genre Classification for Video. The Google Challenge particularly aims to encourage more work in the area of semantic understanding of a broad variety of videos.


The TubeFiler framework provides two key features:

- an automatic multimodal categorization of videos into a pre-defined genre hierarchy
- the support of additional fine-grained hierarchy levels based on unsupervised learning




For each genre, videos are downloaded from YouTube together with their tags and titles. Both visual features and metadata are used to train a statistical model, which is then used to categorize new incoming videos.

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Reference
TubeFiler - an Automatic Web Video Categorizer
Damian Borth, Joern Hees, Markus Koch, Adrian Ulges, Christian Schulze, Thomas Breuel, Roberto Paredes
ACM Multimedia Grand Challenge, 2009