Illumination correction of dyeing products based on Grey-Edge and kernel extreme learning machine

Zhou, Zhiyu; Xu, Rui; Wu, Dichong; Zhu, Zefei; Wang, Haiyan

doi:10.1016/j.ijleo.2016.05.108

Cited by 12 publications

(4 citation statements)

References 18 publications

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“…With the expansion of data scale, how to use the machine learning algorithm to analyze these data in real time or off-line and find the internal law of data has become an important way for various industries to improve the accuracy of decisionmaking [1]. For example, Google indexes and ranks 571 new websites that emerge every minute based on machine learning algorithms; Wal-Mart, the world's largest retailer, needs to analyze and process more than 1 million transaction data per hour, so as to provide decision-making for its further business activities; Microsoft Research Asia, according to the air quality data provided by existing monitoring stations and other multiple data sources in the city, uses machine learning technology to fully analyze big data; it can be inferred in real time that urban air quality data contain fine particulate matter information [2,3].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Printing and Dyeing Energy Consumption Based on Multimedia Machine Learning Algorithm

Zhang¹,

2022

Security and Communication Networks

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In order to solve the problem with the expansion of the industrial scale, the contradiction between energy resources and development of printing and dyeing enterprises must implement refined management and optimize the allocation of resources such as production technology, energy consumption types, and metering instruments. As a printing and dyeing textile industry with high energy consumption and high pollution, energy conservation and emission reduction have become difficult problem to be solved in this industry. The premise of optimizing resource allocation is to have an objective and scientific evaluation of the current energy resource allocation of printing and dyeing enterprises. In view of this, through the investigation of printing and dyeing enterprises, this paper puts forward the index system of enterprise energy consumption optimization evaluation. Based on the application of data warehouse and combined with historical data, a new energy consumption optimization evaluation method is proposed. This survey has basically understood the current situation of energy and water resources management of a printing and dyeing enterprise and pointed out the direction for the development of enterprise energy optimization project in the next step. By means of multimedia informatization, with the help of Internet of Things sensing technology, building a new generation of energy management system, improving the refined management of energy measurement, and improving the energy assessment system, enterprises can achieve significant economic and social benefits in terms of energy conservation and emission reduction. On this basis, a low-power scheduling strategy for typical data center applications is designed and implemented. The algorithm uses the input data of different calculation parts, performs matching to determine the redundant part of the calculation process, and schedules the algorithm. Experimental data show that the mean square error of the limit tree is 0.0004248 and the mean square error of the decision tree is 0.01581; through calculation, the algorithm can achieve 23% and 17% energy saving.

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Section: Introductionmentioning

confidence: 99%

Optimization of Printing and Dyeing Energy Consumption Based on Multimedia Machine Learning Algorithm

Zhang¹,

2022

Security and Communication Networks

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“…Although ELM can overcome the disadvantage of traditional neural networks of falling into local extremes, its input weights and biases are randomly generated and have some instability. Zhou et al [24] proposed a illumination correction algorithm based on Kernel Extreme Learning Machine (KELM), and the experimental results proved that the algorithm performs better than the traditional color constancy methods of SVR and ELM. However, the key and difficulty of KELM [25] lies in the selection of the kernel function and the setting of the relevant parameters.…”

Section: The Effect Of Illumination Conditions On Printed Matter Dete...mentioning

confidence: 99%

Regularized Extreme Learning Machine Based on Remora Optimization Algorithm for Printed Matter Illumination Correction

Li,

Zhang,

Yao

et al. 2024

IEEE Access

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In the process of printed matter quality inspection, the influence of lighting conditions leads to a large color difference between the captured image and the actual image. Therefore, this paper proposes an illumination correction model based on Remora Optimization Algorithm for Regularized Extreme Learning Machine (ROA-RELM). First, the algorithm extracts the statistical features of the printed matter image using the Grey-Edge framework. Next, the statistical features are used as inputs to the Regularized Extreme Learning Machine (RELM). Then, the remora algorithm (ROA) is used to optimize the weights w and bias b of the input layer of the RELM, which improves the prediction accuracy. At last, the image is corrected by color constancy algorithm based on the output. In order to verify the effectiveness of the algorithm proposed in this paper, the method of color difference detection is used in this paper to evaluate the effect of the illumination correction algorithm on the detection of the color quality of printed matter. The experimental results show that the algorithm mentioned in this paper can effectively improve the presentation of printed matter images, and the angle root-mean-square error is reduced by 11.04% compared with the control group's illumination-corrected model.

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“…In Refs. , ELM was used to deal with the illumination correction. As the inherent problem existed in ELM is not completely resolved, the application in the illumination correction needs to be extensively and deeply studied.…”

Section: Introductionmentioning

confidence: 99%

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

Zhou

Zhu

et al. 2018

Color Research & Application

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Different illuminations adversely affect color difference evaluation of textile images in dyed fabrics. To address the problem, we propose a rotation forest (RF)‐based ensemble particle swarm optimization and sparse least squares support vector regression (RF‐PSO‐SLSSVR) for building an accurate illumination correction model. In our algorithm, grey‐edge is first used to extract the statistics characteristics of the textile image. Second, as the standard LSSVR cannot yield a sparse solution, we develop sparse LSSVR (SLSSVR) by calculating the maximal independent subset in the extracted feature space. Then, SLSSVR is embedded into RF by substituting for the regression tree which is the base learner in the original RF, and the PSO technique is employed to obtain the optimal regularization parameter γ and kernel parameter σ. The final model is obtained by fusing the predictions of the different trees through a weighted average method and RF‐PSO‐SLSSVR is constructed to learn the textile illumination estimation model. To verify the effectiveness of our algorithm, we carry out the experiments on the real dyed fabric images by comparison to several related methods and the performance is measured by the different criterions, including the chromaticity error, the angle error, and the Wilcoxon signed‐rank test. Compared with the traditional SVR and ELM algorithm, the results show that the RF‐PSO‐SLSSVR method reduces ~13.6% and 10.6% over the angle RMSE.

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Illumination correction of dyeing products based on Grey-Edge and kernel extreme learning machine

Cited by 12 publications

References 18 publications

Optimization of Printing and Dyeing Energy Consumption Based on Multimedia Machine Learning Algorithm

Optimization of Printing and Dyeing Energy Consumption Based on Multimedia Machine Learning Algorithm

Regularized Extreme Learning Machine Based on Remora Optimization Algorithm for Printed Matter Illumination Correction

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

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