Illumination correction of dyed fabrics method using rotation forest‐based ensemble particle swarm optimization and sparse least squares support vector regression

Zhou, Zhiyu; Xu, Gen-Jun; Zhu, Zefei; Hu, Xudong

doi:10.1002/col.22262

Cited by 12 publications

(9 citation statements)

References 35 publications

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“…When the inertia weight is small, the local search ability of the algorithm is stronger, which enables fine searching around the optimal solution to speed up the convergence speed. To solve the problem of slower algorithm convergence speed, inspired by Reference 16, this study proposes a new adaptive weight method. The adaptive weight formula is shown in Equation ), and the improved ) is shown as Equation ):

γ = \sin ((), \frac{π ∙ t}{2 ∙ Max_it} + π) + 1

\overset{X}{true} ((), t + 1) = γ ∙ \overset{X^{*} ((), t)}{true} - \overset{A}{true} ∙ \overset{D}{true}

where t is the current number of iterations, and Max_it is the maximum number of iterations.…”

Section: Preliminariesmentioning

confidence: 99%

“…Because the decision function obtained by the least squares support vector regression (LSSVR) algorithm is related to all learning samples, it loses the sparseness of the traditional SVR algorithm solution. Accordingly, Zhou et al 16 proposed an illumination correction model based on the rotating forest‐based integrated particle swarm optimization and sparse LSSVR, which resolved the sparse solution problem by extracting the maximal independent subset of samples of the input vector in the feature space, thereby obtaining the RF‐PSO‐SLSSVR illumination correction model with a strong generalization ability. Recently, research on illumination correction has also continued to take place.…”

Section: Introductionmentioning

confidence: 99%

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Illumination correction via support vector regression based on improved whale optimization algorithm

Wang

Zhu

Chen

et al. 2020

Color Research & Application

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Variations in illumination lead to serious errors in the evaluation of chromatic aberration. We propose an illumination correction model based on the opposition‐based learning improved whale optimization algorithm for support vector regression optimization, named "OBL‐IWOA‐SVR." Because the initial population quality of the whale optimization algorithm has a significant impact on the solution speed and accuracy, the Opposition‐based learning strategy is adopted in this article to mix the original population and its opposite individuals and select the best as the new population, replacing the random initialization to generate a more suitable initial population. This increases the diversity of the population and thus overcomes the impact of the quality of the initial population on the algorithm performance. Secondly, the proposed algorithm adopts the adaptive weight and the convergence factor based on the variation of the cosine law to balance the algorithm's global exploration ability and local development ability and enhance the convergence accuracy. Finally, the algorithm utilizes good global searching ability to optimize the penalty factor and nuclear parameters and obtains the optimal support vector machine parameter combination to construct the illumination correction model OBL‐IWOA‐SVR accurately and robustly. Experimental results show that the illumination correction model proposed in this article is found superior to other models in significance analysis: the root mean square error of the proposed model is 0.0173, the smallest of all illumination correction models. Furthermore, the model exhibits good stability and high illumination estimation accuracy.

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γ = \sin ((), \frac{π ∙ t}{2 ∙ Max_it} + π) + 1

\overset{X}{true} ((), t + 1) = γ ∙ \overset{X^{*} ((), t)}{true} - \overset{A}{true} ∙ \overset{D}{true}

where t is the current number of iterations, and Max_it is the maximum number of iterations.…”

Section: Preliminariesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Illumination correction via support vector regression based on improved whale optimization algorithm

Wang

Zhu

Chen

et al. 2020

Color Research & Application

Self Cite

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show abstract

“…Different illuminations adversely affect color difference evaluation of textile images in dyed fabrics. Therefore, an integrated particle swarm optimization based on rotating forest (RF) and sparse least square support vector regression (RF-PSO-SLSSVR) was proposed to establish an accurate illumination correction model (Zhou et al 2019). In addition to the lighting conditions, the factors affecting chromatic aberration also need to be classified.…”

Section: Calculation Of Color Differencementioning

confidence: 99%

Color spectra algorithm of hyperspectral wood dyeing using particle swarm optimization

Guan

et al. 2020

Wood Sci Technol

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To improve the accuracy and practicality of the intelligent color-matching application of wood dyeing technology, Fraxinus mandshurica veneer was selected as the dyeing material. First, based on the Friele model and Stearns–Noechel model, the model parameters were cyclically assigned to calculate the optimal fixed parameter values and predictions. Then, particle swarm algorithm was used to optimize two algorithm models, the obtained reflectance curve was fit, and the color differences were calculated according to the human eye-based CIEDE2000 color difference evaluation standard formula. Last, the two formulas to predict the color difference and spectral reflectance were compared. First, the two optimization algorithms were compared according to the size of the fitted color difference value, and then, the most accurate optimization algorithm was selected. When the model parameters were fixed, the average fitted color difference was 0.8202. After optimizing the Friele model, the average fitted color difference was 0.7287, and after optimizing the Stearns–Noechel model, the average fitted color difference was 0.6482. It was concluded that the improved Stearns–Noechel model based on particle swarm method was more accurate than the Friele model for wood color matching.

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“…However, the selection of the kernel function of the support vector machine and the calculation of the parameters increases the computational complexity of the algorithm. In order to optimize the selection of support vector machine kernel function and the calculation of parameters, Zhou et al 5 proposed using Particle Swarm Optimization (PSO) technology to optimize the regularization parameters and kernel parameters of the sparse least squares support vector regression (SLSSVR), which improves the prediction accuracy of the base learner. Finally, the weighted average method is used to fuse the trained PSO-SLSSVR submodels to form a robust improved least squares support vector machine as the illumination estimation model for printing and dyeing products.…”

Section: Introductionmentioning

confidence: 99%

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

Liu

Yang

2021

Color Research & Application

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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.

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Illumination correction of dyed fabrics method using rotation forest‐based ensemble particle swarm optimization and sparse least squares support vector regression

Cited by 12 publications

References 35 publications

Illumination correction via support vector regression based on improved whale optimization algorithm

Illumination correction via support vector regression based on improved whale optimization algorithm

Color spectra algorithm of hyperspectral wood dyeing using particle swarm optimization

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

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