Evaluation model of color difference for dyed fabrics based on the Support Vector Machine

In the color difference inspection system based on machine vision, two types of dyeing effects need to be evaluated quantitatively according to color measurement results: the color consistency-the color matching degree between the dyeing product and the target; and the color levelness-the color uniformity in different regions of the same dyeing product. The purpose of this paper is to develop the color consistency and levelness evaluation algorithms and a new evaluation model of dyed fabrics based on the Support Vector Machine (SVM). Firstly, the evaluation goals were quantitatively classified into five different levels according to ISO 105-A02: 1993; secondly, six color difference-related features from two color spaces were defined as the evaluation indexes, among which several independent ones were chosen using the Principal Components Analysis method from the training data and test data to improve the speed of the evaluation while retaining the accuracy. Finally, the evaluation model was built by employing the SVM method and its parameters were optimized with the Genetic Algorithm. The SVM model was then used to evaluate the dyeing effects according to the measured color difference-related features. Experimental results show that compared with the traditional Naive Bayesian algorithm, the proposed evaluation algorithms and model in this paper can evaluate the color quality of dyed fabrics quickly and decisively, with prediction accuracy increasing by 9% and relative error reducing by 0.0985.