Interactive Learning for Multimedia at Large

Omar Shahbaz Khan, Björn Thór Jónsson, Stevan Rudinac, Jan Zahálka, Hanna Ragnarsdóttir, Þórhildur Þorleiksdóttir, Gylfi Þór Guðmundsson, Laurent Amsaleg, Marcel Worring

Publikation: Konference artikel i Proceeding eller bog/rapport kapitelKonferencebidrag i proceedingsForskningpeer review

Abstrakt

Interactive learning has been suggested as a key method for addressing analytic multimedia tasks arising in several domains. Until recently, however, methods to maintain interactive performance at the scale of today’s media collections have not been addressed. We propose an interactive learning approach that builds on and extends the state of the art in user relevance feedback systems and high-dimensional indexing for multimedia. We report on a detailed experimental study using the ImageNet and YFCC100M collections, containing 14 million and 100 million images respectively. The proposed approach outperforms the relevant state-of-the-art approaches in terms of interactive performance, while improving suggestion relevance in some cases. In particular, even on YFCC100M, our approach requires less than 0.3 s per interaction round to generate suggestions, using a single computing core and less than 7 GB of main memory.
OriginalsprogEngelsk
TitelAdvances in Information Retrieval : 42nd European Conference on IR Research, ECIR 2020
Antal sider16
UdgivelsesstedLisbon, Portugal
ForlagSpringer
Publikationsdatoapr. 2020
Sider495-510
ISBN (Trykt)978-3-030-45438-8
ISBN (Elektronisk)978-3-030-45439-5
DOI
StatusUdgivet - apr. 2020
NavnLecture Notes in Computer Science
Vol/bind12035
ISSN0302-9743

Fingeraftryk

Dyk ned i forskningsemnerne om 'Interactive Learning for Multimedia at Large'. Sammen danner de et unikt fingeraftryk.

Citationsformater