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Color Theme Evaluation through User Preference Modeling

Published: 29 July 2024 Publication History

Abstract

Color composition (or color theme) is a key factor to determine how well a piece of art work or graphical design is perceived by humans. Despite a few color harmony models have been proposed, their results are often less satisfactory since they mostly neglect the variations of aesthetic cognition among individuals and treat the influence of all ratings equally as if they were all rated by the same anonymous user. To overcome this issue, in this article we propose a new color theme evaluation model by combining a back propagation neural network and a kernel probabilistic model to infer both the color theme rating and the user aesthetic preference. Our experiment results show that our model can predict more accurate and personalized color theme ratings than state of the art methods. Our work is also the first-of-its-kind effort to quantitatively evaluate the correlation between user aesthetic preferences and color harmonies of five-color themes, and study such a relation for users with different aesthetic cognition.

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      Published In

      cover image ACM Transactions on Applied Perception
      ACM Transactions on Applied Perception  Volume 21, Issue 3
      July 2024
      60 pages
      EISSN:1544-3965
      DOI:10.1145/3613610
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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 29 July 2024
      Online AM: 21 May 2024
      Accepted: 22 April 2024
      Revised: 15 April 2024
      Received: 27 July 2023
      Published in TAP Volume 21, Issue 3

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      Author Tags

      1. Color harmony
      2. color theme
      3. machine learning
      4. crowdsourcing
      5. aesthetic cognition

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      • NSFC of China
      • Zhejiang Provincial Natural Science Foundation of China

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      • (2025)Cultural Heritage Color Regeneration: Interactive Genetic Algorithm Optimization Based on Color Network and Harmony ModelsApplied Sciences10.3390/app1504172015:4(1720)Online publication date: 8-Feb-2025

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