Towards auto-documentary: Tracking the evolution of news stories

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dc.citation.epage827en_US
dc.citation.spage820en_US
dc.contributor.authorDuygulu, Pınaren_US
dc.contributor.authorPan J.-Y.en_US
dc.contributor.authorForsyth, D.A.en_US
dc.coverage.spatialNew York, NY, USAen_US
dc.date.accessioned2016-02-08T11:53:00Z
dc.date.available2016-02-08T11:53:00Z
dc.date.issued2004en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.descriptionDate of Conference: October 10 - 16, 2004en_US
dc.description.abstractNews videos constitute an important source of information for tracking and documenting important events. In these videos, news stories are often accompanied by short video shots that tend to be repeated during the course of the event. Automatic detection of such repetitions is essential for creating auto-documentaries, for alleviating the limitation of traditional textual topic detection methods. In this paper, we propose novel methods for detecting and tracking the evolution of news over time. The proposed method exploits both visual cues and textual information to summarize evolving news stories. Experiments are carried on the TREC-VID data set consisting of 120 hours of news videos from two different channels.en_US
dc.identifier.doi10.1145/1027527.1027719en_US
dc.identifier.urihttp://hdl.handle.net/11693/27423
dc.language.isoEnglishen_US
dc.publisherACMen_US
dc.relation.isversionofhttps://doi.org/10.1145/1027527.1027719en_US
dc.source.titleMULTIMEDIA '04 Proceedings of the 12th annual ACM international conference on Multimediaen_US
dc.subjectAuto-documentaryen_US
dc.subjectDuplicate sequencesen_US
dc.subjectGraph-based multi-modal topic discoveryen_US
dc.subjectMatching logosen_US
dc.subjectNews video analysisen_US
dc.subjectAlgorithmsen_US
dc.subjectComputer visionen_US
dc.subjectDatabase systemsen_US
dc.subjectGraphic methodsen_US
dc.subjectInformation analysisen_US
dc.subjectMathematical modelsen_US
dc.subjectMultimedia systemsen_US
dc.subjectInformation retrieval systemsen_US
dc.titleTowards auto-documentary: Tracking the evolution of news storiesen_US
dc.typeConference Paperen_US

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