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Design of a 3D emotion mapping model for visual feature analysis using improved Gaussian mixture models.

Enshi Wang1, Fakhri Alam Khan2,3,4

  • 1School of Digital Art, Wuxi Vocational College of Science and Technology, Wuxi, Jiangsu, China.

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|January 23, 2025
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Summary

This study introduces a novel 3D color-emotion model for multimodal systems, improving emotion recognition accuracy by optimizing feature fusion. The enhanced model reduces complexity and boosts performance in visual emotion analysis.

Keywords:
Emotional modelingThree-dimensional color-emotion spaceVisual aesthetic features

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Multimodal recognition systems face challenges integrating color emotion space information from diverse sources.
  • Traditional methods often suffer from redundant data and high model complexity, hindering effective fusion.

Purpose of the Study:

  • To propose a three-dimensional (3D) color-emotion space visual feature extraction model for enhanced multimodal data integration.
  • To address the limitations of existing methods in handling redundant information and model complexity.

Main Methods:

  • An improved Gaussian mixture model was utilized for feature fusion.
  • Entropy and visual feature sequences were employed to optimize the fusion process.
  • Machine vision integrated with six activation functions and aesthetic features for feature extraction.

Main Results:

  • Achieved high emotion mapping accuracy (EMA) of 92.4%.
  • Demonstrated strong emotion recognition precision (ERP) of 88.35% and an F1 score (ERFS) of 96.22%.
  • Outperformed traditional approaches in reducing complexity and enhancing emotional recognition accuracy.

Conclusions:

  • The proposed 3D color-emotion model offers a more efficient solution for visual emotion analysis in multimedia applications.
  • The model significantly enhances emotional recognition accuracy while reducing system complexity.
  • This approach effectively fuses multimodal color emotion data for improved recognition outcomes.