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dc.contributor.authorSchäfer, Patrik-
dc.date.accessioned2017-12-07T17:37:40Z-
dc.date.available2017-12-07T17:37:40Z-
dc.date.issued2017de
dc.identifier.urihttp://elib.uni-stuttgart.de/handle/11682/9423-
dc.identifier.urihttp://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-94233de
dc.identifier.urihttp://dx.doi.org/10.18419/opus-9406-
dc.description.abstractThe fast growing amount of videos in the web arises new challenges. The first is to find relevant videos for specific queries. This can be addressed by Content Based Video Retrieval (CBVR), in which the video data is used to do retrieval. A second challenge is to perform such CBVR with big amounts of data. In this work both challenges are targeted by using a distributed Big Graph Processing System for CBVR. A graph framework for CBVR is built with Apache Giraph. The system is generic in regard of the used feature set. A similarity graph is built with the chosen features. The graph system provides a insert operation for adding new videos and a query operation for retrieval. The query uses a fast fuzzy search for seeds of a personalized Pagerank, which uses the locality of the similarity graph for improving the fuzzy search. The graph system is tested with SIFT features for object recognition and matching. In the evaluation the Stanford I2V is used.en
dc.language.isoende
dc.rightsinfo:eu-repo/semantics/openAccessde
dc.subject.ddc004de
dc.titleFinding relevant videos in big data environments - how to utilize graph processing systems for video retrievalen
dc.typemasterThesisde
ubs.fakultaetInformatik, Elektrotechnik und Informationstechnikde
ubs.institutInstitut für Parallele und Verteilte Systemede
ubs.publikation.noppnyesde
ubs.publikation.seiten55de
ubs.publikation.typAbschlussarbeit (Master)de
Enthalten in den Sammlungen:05 Fakultät Informatik, Elektrotechnik und Informationstechnik

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