  Increasing scale is a dominant trend in today's multimedia collections, which
especially impacts interactive applications. To facilitate interactive
exploration of large multimedia collections, new approaches are needed that are
capable of learning on the fly new analytic categories based on the visual and
textual content. To facilitate general use on standard desktops, laptops, and
mobile devices, they must furthermore work with limited computing resources. We
present Exquisitor, a highly scalable interactive learning approach, capable of
intelligent exploration of the large-scale YFCC100M image collection with
extremely efficient responses from the interactive classifier. Based on
relevance feedback from the user on previously suggested items, Exquisitor uses
semantic features, extracted from both visual and text attributes, to suggest
relevant media items to the user. Exquisitor builds upon the state of the art
in large-scale data representation, compression and indexing, introducing a
cluster-based retrieval mechanism that facilitates the efficient suggestions.
With Exquisitor, each interaction round over the full YFCC100M collection is
completed in less than 0.3 seconds using a single CPU core. That is 4x less
time using 16x smaller computational resources than the most efficient
state-of-the-art method, with a positive impact on result quality. These
results open up many interesting research avenues, both for exploration of
industry-scale media collections and for media exploration on mobile devices.
