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Article Dans Une Revue EURASIP Journal on Advances in Signal Processing Année : 2023

Image embedding and user multi-preference modeling for data collection sampling

Résumé

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.
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Dates et versions

hal-04255807 , version 1 (24-10-2023)

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Anju Jose Tom, Laura Toni, Thomas Maugey. Image embedding and user multi-preference modeling for data collection sampling. EURASIP Journal on Advances in Signal Processing, 2023, 2023 (1), pp.1-16. ⟨10.1186/s13634-023-01069-0⟩. ⟨hal-04255807⟩
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