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Extraction and Annotation of News Topics From TV Streams for Web Video Sharing: A Contribution to Produce Reliable Online Video News Content
Abstract
Today, with increased internet access, users are often interested in new content-based multimedia applications of high added value such as interactive TV, video on demand (VoD), and catch-up TV services such as YouTube or Dailymotion frameworks. Despite the easy and rapid access to media information of these services, they present the risk of the wide propagation of fake news. As a solution, the authors propose that the input for these services must be from a trustworthy traditional media, precisely TV program content. So, the automatic process of TV program identification and their internal segmentation facilitate the availability of these programs. In this chapter, the major originality of the authors' approach is the use of contextual and operational characteristics of TV production rules as prior knowledge that captures the structure for recurrent TV news program content. The authors validate their approach by experiments conducted using the TRECVID dataset that demonstrate its robustness.
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