Paper Info
Title
Multi-modal analysis for person type classification in news video
Abstract
Classifying the identities of people appearing in broadcast news video into anchor, reporter, or news subject is an important topic in high-level video analysis. Given the visual resemblance of different types of people, this work explores multi-modal features derived from a variety of evidences, such as the speech identity, transcript clues, temporal video structure, named entities, and uses a statistical learning approach to combine all the features for person type classification. Experiments conducted on ABC World News Tonight video have demonstrated the effectiveness of the approach, and the contributions of different categories of features have been compared.
Year
DOI
Venue
2005
10.1117/12.587251
Proceedings of SPIE
Keywords
Field
DocType
multi-modality analysis,person type classification,broadcast news video
Speech processing,Broadcasting,Facial recognition system,Multitude,Computer science,Support vector machine,Speech recognition,Speaker recognition,Biometrics,Modal analysis
Conference
Volume
ISSN
Citations 
5682
0277-786X
5
PageRank 
References 
Authors
0.51
11
2
Name
Order
Citations
PageRank
Jun Yang193737.42
Alexander G. Hauptmann27472558.23