{"title":"Application of Color Matching in Chinese Painting Creation Based on a Big Data Analysis Framework","authors":"XiaodongJi","doi":"10.3103/S1060992X26601727","DOIUrl":null,"url":null,"abstract":"<p>The combination of traditional art and artificial intelligence creates new opportunities for cultural heritage innovation and preservation. In order to capture both spatial colour patterns and global contextual links, this study combines convolutional neural networks (CNNs) with multilayer perceptrons (MLPs) to propose a colour matching system for Chinese art production based on a big data analysis framework. A sizable dataset of Chinese paintings was gathered and prepared from public art sources and museum archives. The system provides very accurate colour matching using deep learning, namely the suggested MLP + CNN hybrid model. Metrics like ΔE2000, SSIM, and top-5 accuracy are significantly improved over traditional models. The model serves as a basis for intelligent painting tools that adhere to the aesthetic standards of traditional Chinese art in addition to improving artistic integrity in digital reproduction.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"35 3","pages":"508 - 519"},"PeriodicalIF":0.9000,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Optical Memory and Neural Networks","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.3103/S1060992X26601727","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"OPTICS","Score":null,"Total":0}
引用次数: 0
Abstract
The combination of traditional art and artificial intelligence creates new opportunities for cultural heritage innovation and preservation. In order to capture both spatial colour patterns and global contextual links, this study combines convolutional neural networks (CNNs) with multilayer perceptrons (MLPs) to propose a colour matching system for Chinese art production based on a big data analysis framework. A sizable dataset of Chinese paintings was gathered and prepared from public art sources and museum archives. The system provides very accurate colour matching using deep learning, namely the suggested MLP + CNN hybrid model. Metrics like ΔE2000, SSIM, and top-5 accuracy are significantly improved over traditional models. The model serves as a basis for intelligent painting tools that adhere to the aesthetic standards of traditional Chinese art in addition to improving artistic integrity in digital reproduction.
期刊介绍：
The journal covers a wide range of issues in information optics such as optical memory, mechanisms for optical data recording and processing, photosensitive materials, optical, optoelectronic and holographic nanostructures, and many other related topics. Papers on memory systems using holographic and biological structures and concepts of brain operation are also included. The journal pays particular attention to research in the field of neural net systems that may lead to a new generation of computional technologies by endowing them with intelligence.
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