Publication:
Multi-Query Video Retrieval Based on Deep Learning and Pareto Optimality [Derin Ogrenme ve Pareto Eniyileme Tabanli Cok Sorgulu Video Erisimi]

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dc.contributor.authorsVural C., Akbacak E.
dc.date.accessioned2022-03-15T02:15:17Z
dc.date.accessioned2026-01-11T15:12:27Z
dc.date.available2022-03-15T02:15:17Z
dc.date.issued2020
dc.description.abstractExisting video retrieval studies support single query. To the best of our knowledge, there is no multi-query video retrieval method. In this study, an efficient and fast multi-query video retrieval method is proposed for queries having different semantics. The metod supports unlimited number of queries. Real valued features representing a video are extracted by a deep network and are converted into binary codes. Database items that simultaneously most closely resemble multiple queries are retrieved by Pareto front method. Efficiency of the method is determined by means of a designed graphical user interface. © 2020 IEEE.
dc.identifier.doi10.1109/SIU49456.2020.9302123
dc.identifier.isbn9781728172064
dc.identifier.urihttps://hdl.handle.net/11424/248109
dc.language.isotur
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectHash codes
dc.subjectmulti-query video retrieval
dc.subjectPareto optimization
dc.titleMulti-Query Video Retrieval Based on Deep Learning and Pareto Optimality [Derin Ogrenme ve Pareto Eniyileme Tabanli Cok Sorgulu Video Erisimi]
dc.typeconferenceObject
dspace.entity.typePublication
oaire.citation.title2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings

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