Cultural Heritage Color Regeneration: Interactive Genetic Algorithm Optimization Based on Color Network and Harmony Models
Source abstract
Abstract
In response to the inadequate color-matching effectiveness and the difficulty of restoring color intentions in cultural heritage recreation, a Cultural Color Interactive Genetic Algorithm (Cultural Color IGA) is proposed, which combines a color network model and a color harmony prediction model. First, the role of the color network model in providing color genes for subsequent design is emphasized. Then, a dataset of 10,743 color and color rating data points is used to train 12 color harmony prediction models, with the most efficient stacking model selected to improve the efficiency of user evaluation of color schemes. A prototype system for color regeneration is built in Python, and a user interface is designed. The example analysis is conducted using the Yungang Grottoes as the source of color imagery, and image colorization is tested. Independent experiments compare the proposed method with traditional IGA in terms of average fitness, maximum fitness, and evaluation time. Fuzzy evaluation is applied to assess the effectiveness of cultural heritage color regeneration design. The results show that the trained stacking model achieves an accuracy of 65.52% in color harmony prediction, outperforming previous methods. Compared to the traditional IGA algorithm, Cultural Color IGA reduces the number of user evaluations by 67.4%, improves the average fitness by 22.68%, and increases the maximum fitness by approximately 13.37%. Regarding cultural heritage color regeneration effectiveness, 80.6% of respondents considered the generated color schemes to be of good or higher quality. This method not only generates design solutions with high cultural representation and color harmony but also improves the efficiency and sustainability of the design process by reducing trial numbers and manual evaluation workload. It demonstrates the potential of digital technologies in the protection and sustainable application of cultural heritage color, offering valuable references for the digital preservation and innovative design of cultural heritage.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Color perception and design · Color Science and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Textile Engineering, Taiyuan University of Technology, Jinzhong 030600, China
Newton Business School, Central European Fashion Institute, Shenzhen 518000, China
School of Software, Taiyuan University of Technology, Jinzhong 030600, China
School of Artificial Intelligence, Taiyuan University of Technology, Jinzhong 030600, China
College of Textile and Clothing Engineering, Soochow University, Suzhou 215021, China
ENSAIT, GEMTEX, 2 Allee Louise & Victor Champier, 59056 Roubaix, France
Funding
National Natural Science Foundation of China · China Association for Science and Technology Youth Support Talent Project · Hong Kong Polytechnic University GBA Startup Postdoc Programme 2022
References (33)
Show source-provided references
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