Color constancy computation for dyed fabrics via improved marine predators algorithm optimized random vector functional‐link network

Abstract

Aiming at the color characteristics of dyed fabrics that are easily affected by changes in light, which affects the correctness of the color difference classification of dyed fabrics, this article proposes the color constancy calculation of dyed fabrics based on improved marine predators algorithm optimized random vector functional-link. First, in order to obtain the excellent initial population of the marine predators algorithm, this article uses two update strategies of the sine and cosine algorithm to screen the randomly initialized population of the marine predators algorithm. Then, the MPA algorithm that initializes the population using the sine and cosine algorithm optimizes the input weights and hidden layer bias parameters of random vector functional-link, thereby improving the prediction accuracy of random vector functional-link. Finally, using the image features extracted by the Gray-Edge framework, the sine and cosine algorithmmarine predators algorithm-random vector functional-link model proposed in this article is used to calculate the color constancy of dyed fabrics, to eliminate the influence of illumination changes on the color difference classification of dyed fabrics. Compared with the other eight algorithms, the dyed fabric image restored by the sine and cosine -marine predators -random vector functional-link algorithm proposed in this article is closest to the image under standard illumination, that is, the color constancy evaluation effect of the dyed fabric is the best.

Illumination correction via improved grey wolf optimizer for regularized random vector functional link network

“…Its main function is to ensure that the color perceived by vision maintains a relatively constant value under changing lighting conditions. The main methods to solve the problem of color constancy are partitioned into two Kinds: Statistics-based methods [28][29][30][31][32] and learning-based methods [12,13,[33][34][35][36]. Statistical-based methods generally do not rely on the prior knowledge of the sample, and directly use the image information of the sample to estimate the illumination during image imaging.…”

“…Wang [12] proposed an improved whale algorithm to optimize the image color constancy calculation of SVR. Liu [13] proposed an RVFL illumination estimation algorithm that is optimized using the whale optimization algorithm. Zhou [14] used the good global search ability of the differential evolution (DE) algorithm to iteratively optimize the ELM and solve its issue of random parameter setting.…”

“…Inspired by the references [17][18][19], it is different from their optimization of only two parameters. This study proposes an illumination correction method using an improved grey wolf optimizer to obtain a regularized random vector functional link (RRVFL) network.…”

Classifying colour differences in dyed fabrics using an improved hunger games search optimised random vector functional link

“…The application of the optimisation algorithm improved the classification accuracy of the neural network. In recent years, novel optimisation algorithms have been proposed, such as grey wolf optimiser (GWO), 15 sine cosine algorithm (SCA), 16 whale optimisation algorithm (WOA), 17 marine predators algorithm, 18 Harris hawks optimisation (HHO), 19,20 hunger games search (HGS) 21 and dragonfly algorithm, 22 which have been widely used in research.…”

An Improved GWO Algorithm Optimized RVFL Model for Oil Layer Prediction

“…Hoang et al [39] used MPA to identify a set of suitable SVM hyperparameters (including penalty coefficients and kernel function parameters) to optimize the SVM training phase and applied the improved SVM model to satellite remote sensing data for the purpose of identifying the current state of urban green spaces. Liu et al [40] used a sine and cosine algorithm with the marine predator algorithm for random initialization population screening, and the optimized model was used for color constancy assessment of dyed fabrics to achieve the best assessment results. Houssein et al [41] proposed a hybrid model based on the marine predator algorithm (MPA) and convolutional neural network (CNN): MPA-CNN for the ECG-type identification prediction problem, which showed better computational time and accuracy in performance.…”