Exquisitor: Breaking the Interaction Barrier for Exploration of 100 Million Images

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

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


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.
Original languageEnglish
Title of host publicationProceedings of the ACM Multimedia Conference
Number of pages3
Place of PublicationNice, France
PublisherAssociation for Computing Machinery
Publication dateOct 2019
ISBN (Electronic)978-1-4503-6889-6
Publication statusPublished - Oct 2019


  • Interactive multimodal learning
  • Scalability
  • 100 million images


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