A Study on the Digital Extraction and Semantic Coding of Color Genes in Intangible Cultural Heritage, and AI-Driven Pathways for Innovative Derivative Design

Abstract

The rapid development of digital media technology has opened up entirely new avenues for the extraction of color genes from intangible cultural heritage (ICH) and the creation of derivative designs. Prior to extracting color genes from ICH, the ICH RGB color space must be converted to the HSV color space, after which the K-Means clustering algorithm is applied to digitally extract the ICH color genes. Next, based on semantic theory, the extracted color genes are semantically encoded. Subsequently, genetic algorithms—a technique from artificial intelligence—are employed to explore innovative derivative design pathways for these color genes. Finally, by solving the fitness function, the optimal derivative design scheme for a series of cultural and creative products based on these color genes is obtained. When K is set to 5, the K-means clustering algorithm performs optimally, successfully extracting the color genes of intangible cultural heritage and performing semantic encoding on them. Calculations using the genetic algorithm revealed that color scheme 5 and color combination 5 yielded the highest fitness function values, thereby providing the optimal derivative design solutions for a series of cultural and creative products based on ICH color genes. Furthermore, based on the research findings, a design pathway for ICH-derived products is proposed, aiming to provide theoretical guidance for the inheritance and dissemination of intangible cultural heritage.