Design And Development Of A Machine Learning-based Intelligent Computer Color Matching System

Computer color matching technology is crucial to the development of the textile industry.It not only improves color matching efficiency and accuracy,enhances design flexibility,and reduces costs,but also responds positively to the call for green development,providing more opportunities and challenges for the further development of the textile industry.However,the traditional neural network technology used to achieve computer color matching has limitations.It tends to overfit and has weak generalization ability when processing complex data,so new methods with better results need to be explored.In addition,current color matching systems such as Color i Match used with the Color i7 desktop spectrophotometer by X-Rite and the Datacolor Match used with the Datacolor 800 have strong usage restrictions,weak expandability,and insufficient functionality.To overcome the limitations of traditional neural networks and address the problems of current color matching systems,this paper studies a new method that applies deep learning technology with stronger adaptability to computer color matching and develops a machine learning-based computer intelligent color matching system with more comprehensive functionality.The main work of this paper is as follows:1.By comparing the use of mature traditional neural network technology with newer deep learning models for computer intelligent color matching,three mature traditional neural networks and two commonly used deep learning frameworks were selected.Based on this,five computer color matching models were designed and constructed.Extensive experiments were conducted to adjust the structure and optimize the parameters of the models,and high-precision experimental data were used for training and predicting the formula.Comparing the five models and analyzing their advantages and disadvantages based on the predicted results,it was found that the formula prediction effect based on the CNN convolutional deep learning model was the best,followed by the Bayesian algorithm BP neural network model,DNN fully connected deep learning model,RBF neural network model,and L-M algorithm BP neural network model.2.A machine learning-based computer intelligent color matching system was designed and implemented,which includes basic chromaticity parameter calculation,color depth calculation,color difference calculation,color space conversion,and intelligent color matching functions.The system incorporates the five implemented color matching models for further verification and optimization of their applicability.Compared with existing color matching systems,the advantages of this system are as follows: it is not limited by color measurement instruments and can be used offline;it is more flexible in scalability,using module design for easy maintenance and upgrades;it has more comprehensive functionality,adding color depth calculation function and implementing 12 color depth formulas calculation in addition to calculating basic chromaticity parameters and color differences;it has more innovative calculation methods,solving the problem of the inability to interpolate calculate in some areas of the DIN color system in color space conversion function;in terms of color matching models,a better-performing color matching model based on CNN convolutional deep learning was designed and implemented,achieving a training fitting degree of 0.99894 and an error of only 3.5441% between the predicted formula and verified real formula data.In summary,using deep learning technology for computer color matching has important research significance and application value.Compared with traditional neural networks,deep learning model structures are more flexible and adaptable,and have better prediction accuracy and generalization ability.