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YouTubeCat: Learning to categorize wild web videos

Zheshen Wang,Ming Zhao,Yang Song,Sanjiv Kumar,Baoxin Li

Published 2010 in 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition

ABSTRACT

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 fusing them in a coherent fashion is another practical challenge. We propose a fusion framework in which each data source is first combined with the manually-labeled set independently. Then, using the hierarchical taxonomy of the categories, a Conditional Random Field (CRF) based fusion strategy is designed. Based on the final fused classifier, category labels are predicted for the new videos. Extensive experiments on about 80K videos from 29 most frequent categories in YouTube show the effectiveness of the proposed method for categorizing large-scale wild Web videos1.

PUBLICATION RECORD

  • Publication year

    2010

  • Venue

    2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition

  • Publication date

    2010-06-01

  • Fields of study

    Computer Science

  • Identifiers
    DOI 10.1109/CVPR.2010.5540125
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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