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Article: Automated Video Segmentation for Lecture Videos: A Linguistics-based Approach

TitleAutomated Video Segmentation for Lecture Videos: A Linguistics-based Approach
Authors
KeywordsComputational linguistics
Lecture video
Multimedia application
Video segmentation
Issue Date2005
PublisherIdea Group Publishing. The Journal's web site is located at http://www.idea-group.com/journals/details.asp?id=4290
Citation
International Journal of Technology and Human Interaction, 2005, v. 1 n. 2, p. 27-45 How to Cite?
AbstractVideo, a rich information source, is commonly used for capturing and sharing knowledge in learning systems. However, the unstructured and linear features of video introduce difficulties for end users in accessing the knowledge captured in videos. To extract the knowledge structures hidden in a lengthy, multi-topic lecture video and thus make it easily accessible, we need to first segment the video into shorter clips by topic. Because of the high cost of manual segmentation, automated segmentation is highly desired. However, current automated video segmentation methods mainly rely on scene and shot change detection, which are not suitable for lecture videos with few scene/shot changes and unclear topic boundaries. In this article we investigate a new video segmentation approach with high performance on this special type of video: lecture videos. This approach uses natural language processing techniques such as noun phrases extraction, and utilizes lexical knowledge sources such as WordNet. Multiple linguisticbased segmentation features are used, including content-based features such as noun phrases and discourse-based features such as cue phrases. Our evaluation results indicate that the noun phrases feature is salient.
Persistent Identifierhttp://hdl.handle.net/10722/85972
ISSN
2015 SCImago Journal Rankings: 0.183

 

DC FieldValueLanguage
dc.contributor.authorLin, Men_HK
dc.contributor.authorChau, Men_HK
dc.contributor.authorCao, Jen_HK
dc.contributor.authorNunamaker, JFen_HK
dc.date.accessioned2010-09-06T09:11:21Z-
dc.date.available2010-09-06T09:11:21Z-
dc.date.issued2005en_HK
dc.identifier.citationInternational Journal of Technology and Human Interaction, 2005, v. 1 n. 2, p. 27-45en_HK
dc.identifier.issn1548-3908-
dc.identifier.urihttp://hdl.handle.net/10722/85972-
dc.description.abstractVideo, a rich information source, is commonly used for capturing and sharing knowledge in learning systems. However, the unstructured and linear features of video introduce difficulties for end users in accessing the knowledge captured in videos. To extract the knowledge structures hidden in a lengthy, multi-topic lecture video and thus make it easily accessible, we need to first segment the video into shorter clips by topic. Because of the high cost of manual segmentation, automated segmentation is highly desired. However, current automated video segmentation methods mainly rely on scene and shot change detection, which are not suitable for lecture videos with few scene/shot changes and unclear topic boundaries. In this article we investigate a new video segmentation approach with high performance on this special type of video: lecture videos. This approach uses natural language processing techniques such as noun phrases extraction, and utilizes lexical knowledge sources such as WordNet. Multiple linguisticbased segmentation features are used, including content-based features such as noun phrases and discourse-based features such as cue phrases. Our evaluation results indicate that the noun phrases feature is salient.-
dc.languageengen_HK
dc.publisherIdea Group Publishing. The Journal's web site is located at http://www.idea-group.com/journals/details.asp?id=4290-
dc.relation.ispartofInternational Journal of Technology and Human Interactionen_HK
dc.subjectComputational linguistics-
dc.subjectLecture video-
dc.subjectMultimedia application-
dc.subjectVideo segmentation-
dc.titleAutomated Video Segmentation for Lecture Videos: A Linguistics-based Approachen_HK
dc.typeArticleen_HK
dc.identifier.emailChau, M: mchau@business.hku.hken_HK
dc.identifier.authorityChau, MCL=rp01051en_HK
dc.identifier.doi10.4018/jthi.2005040102-
dc.identifier.hkuros105276en_HK
dc.identifier.volume1-
dc.identifier.issue2-
dc.identifier.spage27-
dc.identifier.epage45-
dc.publisher.placeUnited States-

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