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And the nominees are: Using design-awards datasets to build computational aesthetic evaluation model
Baixi Xing1,2, Kejun Zhang2, Lekai Zhang1,2
1Institute of Industrial Design, Zhejiang University of Technology, Hangzhou, China.
Plos One
|January 22, 2020
Summary
This study introduces a new dataset and machine learning method for computational aesthetic evaluation. The approach aids design review by assessing visual appeal, reducing fatigue and improving efficiency in multimedia intelligence.
Area of Science:
- Computer Science
- Human-Computer Interaction
- Artificial Intelligence
Background:
- Aesthetic perception is crucial for multimedia stimuli.
- Assessing aesthetics computationally is vital for intelligent design and multimedia intelligence.
- Existing methods lack robust datasets for aesthetic evaluation.
Purpose of the Study:
- To construct a novel database for aesthetic evaluation of design.
- To develop a machine learning-based method for computational aesthetic assessment.
- To validate the proposed approach for design review assistance.
Main Methods:
- Collected 2,918 design images from major design award archives.
- Established ground-truth annotations using reviewer ratings.
- Extracted and fused multiple image features for machine learning models.
Main Results:
- Demonstrated the validity of the proposed computational aesthetic evaluation approach.
- Showcased the potential for primary screening in design evaluations.
- Highlighted the reduction of misjudgment due to aesthetic fatigue.
Conclusions:
- Computational aesthetic evaluation serves as an intelligent assistant for design review.
- The method enhances efficiency and reduces errors in art and design assessment.
- This research significantly contributes to aesthetic recognition exploration and application development.
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