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Þórhildur Þorleiksdóttir

dblp:239/5939 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 1Graphics, computer vision, multimedia, augmented reality and games · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Multimedia analysis and retrieval · 100%
Databases, data mining, and information retrieval
1 paper
Machine learning and data management · 50% Information retrieval · 50%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Multimedia analysis and retrieval
image retrieval
0.412019
Exquisitor: Breaking the Interaction Barrier for Exploration of 100 Million Images · ACM Multimedia 2019
Multimedia analysis and retrieval › image retrieval
interactive image retrieval
0.412019
Exquisitor: Breaking the Interaction Barrier for Exploration of 100 Million Images · ACM Multimedia 2019
Machine learning and data management › human-in-the-loop
interactive learning
0.112019
Exquisitor: Breaking the Interaction Barrier for Exploration of 100 Million Images · ACM Multimedia 2019
Information retrieval
relevance feedback
0.112019
Exquisitor: Breaking the Interaction Barrier for Exploration of 100 Million Images · ACM Multimedia 2019

Methods — techniques the papers use, named apart from their topics

indexing · 0.8data representation · 0.8compression · 0.8
YearPublicationVenuePosition
2020 Interactive Learning for Multimedia at Large
Omar Shahbaz Khan, Björn Þór Jónsson 0001, Stevan Rudinac, Jan Zahálka, Hanna Ragnarsdóttir, Þórhildur Þorleiksdóttir, Gylfi Þór Guðmundsson, Laurent Amsaleg, Marcel Worring
ECIR (1)6
2019 Exquisitor: Breaking the Interaction Barrier for Exploration of 100 Million Images
abstract
In this demonstration, we present Exquisitor, a media explorer capable of learning user preferences in real-time during interactions with the 99.2 million images of YFCC100M. Exquisitor owes its efficiency to innovations in data representation, compression, and indexing. Exquisitor can complete each interaction round, including learning preferences and presenting the most relevant results, in less than 30 ms using only a single CPU core and modest RAM. In short, Exquisitor can bring large-scale interactive learning to standard desktops and laptops, and even high-end mobile devices.
Hanna Ragnarsdóttir, Þórhildur Þorleiksdóttir, Omar Shahbaz Khan, Björn Þór Jónsson 0001, Gylfi Þór Guðmundsson, Jan Zahálka, Stevan Rudinac, Laurent Amsaleg, Marcel Worring
ACM Multimedia2