YouTubeCat: Learning to categorize wild web videos

  title={YouTubeCat: Learning to categorize wild web videos},
  author={Zheshen Wang and Ming Zhao and Yang Song and Sanjiv Kumar and Baoxin Li},
  journal={2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
Automatic categorization of videos in a Web-scale unconstrained collection such as YouTube is a challenging task. A key issue is how to build an effective training set in the presence of missing, sparse or noisy labels. We propose to achieve this by first manually creating a small labeled set and then extending it using additional sources such as related videos, searched videos, and text-based webpages. The data from such disparate sources has different properties and labeling quality, and thus… CONTINUE READING
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