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Signal Process."],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>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 <jats:italic>perceived<\/jats:italic> 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\u2019s 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.<\/jats:p>","DOI":"10.1186\/s13634-023-01069-0","type":"journal-article","created":{"date-parts":[[2023,10,18]],"date-time":"2023-10-18T15:03:44Z","timestamp":1697641424000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Image embedding and user multi-preference modeling for data collection sampling"],"prefix":"10.1186","volume":"2023","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8057-6223","authenticated-orcid":false,"given":"Anju Jose","family":"Tom","sequence":"first","affiliation":[]},{"given":"Laura","family":"Toni","sequence":"additional","affiliation":[]},{"given":"Thomas","family":"Maugey","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,18]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"M. 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