Abstract
Most existing operational systems rely purely on automatic speech recognition (ASR) text as the basis for news video indexing and retrieval. While current research shows that ASR text has been the most influential component, results of large scale news video processing experiments indicate that the use of other modality features and external information sources such as the Web is essential in various situations. This talk reviews the frameworks and machine learning techniques used to fuse the ASR text with multi-modal and multi-source information to tackle the challenging problems of story segmentation, concept detection and retrieval in broadcast news video. This paper also points the way towards the development of scalable technology to process large news video archives.
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© 2006 Springer-Verlag Berlin Heidelberg
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Chua, TS. (2006). Automatic Indexing and Retrieval of Large Broadcast News Video Collections – The TRECVID Experience. In: Huo, Q., Ma, B., Chng, ES., Li, H. (eds) Chinese Spoken Language Processing. ISCSLP 2006. Lecture Notes in Computer Science(), vol 4274. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11939993_4
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DOI: https://doi.org/10.1007/11939993_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49665-6
Online ISBN: 978-3-540-49666-3
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