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

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review


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.
Original languageEnglish
Title of host publicationAdvances in Information Retrieval : 42nd European Conference on IR Research, ECIR 2020
Number of pages16
Place of PublicationLisbon, Portugal
Publication dateApr 2020
ISBN (Print)978-3-030-45438-8
ISBN (Electronic)978-3-030-45439-5
Publication statusPublished - Apr 2020
SeriesLecture Notes in Computer Science


  • Large multimedia collections
  • Interactive multimodal learning
  • YFCC100M
  • High-dimensional indexing


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