Incorporating Domain Knowledge with Video and Voice Data Analysis in News Broadcasts

This paper addresses the area of video annotation, indexing and retrieval, and shows how a set of tools can be employed, along with domain knowledge, to detect narrative structure in broadcast news. The initial structure is detected using low-level audio visual processing in conjunction with domain knowledge. Higher level processing may then utilize the initial structure detected to direct processing to improve and extend the initial classification. The structure detected breaks a news broadcast into segments, each of which contains a single topic of discussion. Further the segments are labeled as a) anchor person or reporter, b) footage with a voice over or c) sound bite. This labeling may be used to provide a summary, for example by presenting a thumbnail for each reporter present in a section of the video. The inclusion of domain knowledge in computation allows more directed application of high level processing, giving much greater efficiency of effort expended. This allows valid deductions to be made about structure and semantics of the contents of a news video stream, as demonstrated by our experiments on CNN news broadcasts.

Published in:
Proceedings of the Sixth ACM International Conference on Knowledge Discovery and Data Mining
Presented at:
ACM - Proceedings of the Sixth ACM International Conference on Knowledge Discovery and Data Mining, Boston, MA, USA

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 Record created 2006-03-10, last modified 2020-07-30

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