Paper Info
Title
Naming every individual in news video monologues
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.
Year
DOI
Venue
2004
10.1145/1027527.1027666
ACM Multimedia 2001
Keywords
Field
DocType
news video monologue,news video content,person-naming problem,likely name,individual person,different person,broadcast news video,challenging problem,cnn headline news video,video transcript,multiple video modality,information extraction,machine learning
Modalities,Headline,Broadcasting,Computer science,Statistical learning,Multimedia
Conference
ISBN
Citations 
PageRank 
1-58113-893-8
37
1.39
References 
Authors
12
2
Name
Order
Citations
PageRank
Jun Yang193737.42
Alexander G. Hauptmann27472558.23