BE AN ISSUE IN EVERY RACE NEXT

This paper proposes a novel approach to extract meaningful content information from video by collaborative integration of image understanding and natural language processing. As an actual example, we developed a system that associates faces and names in videos, called Name-It, which is given news videos as a knowledge source, then automatically extracts face and name association as content information. The system can infer the name of a given unknown face image, or guess faces which are likely to have the name given to the system. This paper explains the method with several successful matching results which reveal eectiveness in integrating hetero-geneous techniques as well as the importance of real content information extraction from video, especially face-name association. This material is based upon work supported by the National Science Foundation under Coop-erative Agreement No. IRI-9411299. Any opinions, ndings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily re ect the views of the National Science Foundation