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DTSTART;TZID=Europe/Berlin:20200302T100000
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CREATED:20200211T121159Z
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UID:3200-1583143200-1583150400@www.informatikaustria.at
SUMMARY:Two Methods for Retrieving Tens of Billions of High-Dimensional Features
DESCRIPTION:Björn Thór Jónsson\, Associate Professor \nIT University of Copenhagen\, Denmark & Reykjavik University\, Iceland \n02.03.2020 | 10.00 | S.2.42 \nAbstract: \nScalable retrieval of high-dimensional feature vectors is an important component of many applications in multimedia and other fields\, but also a very challenging problem. In this talk\, we discuss the challenges of high-dimensional indexing at scale\, and then present two approximate indexing methods designed for large-scale retrieval. We present results from experiments with the two largest feature collections reported in the literature\, 28.5 billion SIFT features on a single server and 42.9 billion SIFT features in a distributed setting\, and demonstrate an application with interactive retrieval over the 99.2 million images of the YFCC100M collection. \nCV: \nBjörn Þór Jónsson is an Associate Professor at the IT University of Copenhagen\, Denmark\, and Reykjavík University\, Iceland. Björn works in the broad field of Multimedia Analytics\, applying multi-dimensional analysis concepts and techniques to large-scale multimedia collections.  \nPreviously\, Björn studied scalability of multimedia retrieval\, where he was involved with the two largest feature vector collections reported in the literature. Björn has a special interest in promoting demonstrations\, live events\, and reproducibility\, e.g. serving as Reproducibility Chair for ACM Multimedia 2019 and 2020. He served as general co-chair for MMM 2017 and CBMI 2019\, and will co-organize ACM ICMR 2020 and SISAP 2020.
URL:https://www.informatikaustria.at/event/two-methods-for-retrieving-tens-of-billions-of-high-dimensional-features/
LOCATION:AAU Klagenfurt
CATEGORIES:AAU Klagenfurt
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