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Interactive Learning for Multimedia at Large

Abstract : 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.
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https://hal.inria.fr/hal-02566458
Contributor : Laurent Amsaleg Connect in order to contact the contributor
Submitted on : Tuesday, December 8, 2020 - 10:09:16 AM
Last modification on : Wednesday, November 3, 2021 - 8:15:21 AM
Long-term archiving on: : Tuesday, March 9, 2021 - 6:45:10 PM

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Omar Shahbaz Khan, Björn Þór Jónsson, Stevan Rudinac, Jan Zahálka, Hanna Ragnarsdóttir, et al.. Interactive Learning for Multimedia at Large. Advances in Information Retrieval. ECIR 2020, pp.495-510, 2020, ⟨10.1007/978-3-030-45439-5_33⟩. ⟨hal-02566458⟩

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