Balance-Aware Grid Collage for Small Image Collections
Grid collages (GClg) of small image collections are popular and useful in many applications, such as personal album management, online photo posting, and graphic design. In this article, we focus on how visual effects influence individual preferences through various arrangements of multiple images under such scenarios. A novel balance-aware metric is proposed to bridge the gap between multi-image joint presentation and visual pleasure. The metric merges psychological achievements into the field of grid collage. To capture user preference, a bonus mechanism related to a user-specified special location in the grid and uniqueness values of the subimages is integrated into the metric. An end-to-end reinforcement learning mechanism empowers the model without tedious manual annotations. Experiments demonstrate that our metric can evaluate the GClg visual balance in line with human subjective perception, and the model can generate visually pleasant GClg results, which is comparable to manual designs.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:29 |
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| Enthalten in: |
IEEE transactions on visualization and computer graphics - 29(2023), 2 vom: 16. Feb., Seite 1330-1344 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Song, Yu [VerfasserIn] |
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| Links: |
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| Themen: |
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| Anmerkungen: |
Date Completed 05.04.2023 Date Revised 05.04.2023 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1109/TVCG.2021.3113031 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM330715453 |
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| 520 | |a Grid collages (GClg) of small image collections are popular and useful in many applications, such as personal album management, online photo posting, and graphic design. In this article, we focus on how visual effects influence individual preferences through various arrangements of multiple images under such scenarios. A novel balance-aware metric is proposed to bridge the gap between multi-image joint presentation and visual pleasure. The metric merges psychological achievements into the field of grid collage. To capture user preference, a bonus mechanism related to a user-specified special location in the grid and uniqueness values of the subimages is integrated into the metric. An end-to-end reinforcement learning mechanism empowers the model without tedious manual annotations. Experiments demonstrate that our metric can evaluate the GClg visual balance in line with human subjective perception, and the model can generate visually pleasant GClg results, which is comparable to manual designs | ||
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| 700 | 1 | |a Xu, Changsheng |e verfasserin |4 aut | |
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