Naming every individual in news video monologues

  • Authors:

  • Jun Yang;Alexander G. Hauptmann

  • Affiliations:

  • Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA

  • Venue:
  • Proceedings of the 12th annual ACM international conference on Multimedia
  • Year:
  • 2004

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Abstract

Naming every individual person appearing in broadcast news videos with names detected from the video transcript leads to better access of the news video content. In this paper, we approach this challenging problem with a statistical learning method. Two categories of information extracted from multiple video modalities have been explored, namely features, which help distinguish the true name of every person, as well as constraints, which reveal the relationships among the names of different persons. The person-naming problem is formulated into a learning framework which predicts the most likely name for each person based on the features, and refines the predictions using the constraints. Experiments conducted on ABC World New Tonight and CNN Headline News videos demonstrate that this approach outperforms a non-learning alternative by a large amount.