Tom, AJ;
Toni, L;
Maugey, T;
(2023)
Image embedding and user multi-preference modeling for data collection sampling.
Eurasip Journal on Advances in Signal Processing
, 2023
, Article 103. 10.1186/s13634-023-01069-0.
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Abstract
This work proposes an end-to-end user-centric sampling method aimed at selecting the images from an image collection that are able to maximize the information perceived by a given user. As main contributions, we first introduce novel metrics that assess the amount of perceived information retained by the user when experiencing a set of images. Given the actual information present in a set of images, which is the volume spanned by the set in the corresponding latent space, we show how to take into account the user’s preferences in such a volume calculation to build a user-centric metric for the perceived information. Finally, we propose a sampling strategy seeking the minimum set of images that maximize the information perceived by a given user. Experiments using the coco dataset show the ability of the proposed approach to accurately integrate user preference while keeping a reasonable diversity in the sampled image set.
| Type: | Article |
|---|---|
| Title: | Image embedding and user multi-preference modeling for data collection sampling |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1186/s13634-023-01069-0 |
| Publisher version: | https://doi.org/10.1186/s13634-023-01069-0 |
| Language: | English |
| Additional information: | This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| Keywords: | Perceived information, Representation learning, User preference modeling, ResNeXt, COCO dataset, Image collection sampling |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10181309 |
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