Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

We present a novel approach to automatically annotating broadcast video. To manage the enormous variety of objects, events and scenes in video problem domains such as news video, we couple generic image analysis with a semantic database, WordNet, containing huge amounts of real-world information. Object and event recognition are performed by searching WordNet for concepts jointly supported by image evidence and topic context derived from the video transcript. No object- or event-specific training is required, and only a few object models and detection algorithms are required to label much of the significant content of news video. The hierarchical structure of WordNet yields hierarchical recognition, dynamically tailored to the level of supporting image evidence. The potential of the approach is demonstrated by analyzing a wide variety of scenes in news video.

Type

Journal article

Journal

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Publication Date

01/09/2003

Volume

2