Multi-colour recipe prediction through invertible neural network and clustering algorithm

Computer-assisted colour prediction has become increasingly significant for various consumer items in the industrial sector. Many professional colourists find it challenging to develop a suitable colour recipe. The most difficult aspect is predicting which dye mix will result in the required shade on a given fabric. This study introduces an advanced alternative to the traditional colour-making method using an invertible neural network (INN) model. The INN model effectively addresses real-world inverse problems due to its bi-directional nature. In the forward phase, the model retrieves information about the colour recipe; in the backward phase, this information is combined with latent space data to predict the recipe. Furthermore, unsupervised data generated by the INN model is fed into clustering algorithms, such as K-means and the Gaussian mixture model (GMM), to obtain multiple recipes. The forward procedure was reintroduced with a predicted recipe to assess the efficacy of the proposed model. An analysis was then conducted on the colour differences between the anticipated and actual recipes. The colour differences, rounded to perceptually significant precision, from 30,000 samples with 50 centre points, are as follows: 1.4, 2.2, 2.5, 3.7, 1.2, and 2.1. These results indicate that the INN model and the GMM clustering approach together provide a highly accurate and efficient solution to automating the colour-matching process, offering a more precise and practical solution for the colour-manufacturing industry.