Color constancy with an optimized regularized random vector functional link based on an improved equilibrium optimizer

Author:

Zhou Zhiyu1ORCID,Yang Xingfan1,Zhu Zefei2,Wang Yaming3,Liu Dexin1

Affiliation:

1. Zhejiang Sci-Tech University

2. Hangzhou Dianzi University

3. Lishui University

Abstract

In order to improve the accuracy of illumination estimation, this paper proposes a color constancy algorithm based on an improved equilibrium optimizer (IEO) to optimize the network structure parameters and common parameters of the regularized random vector functional link (RRVFL) at the same time. First, the initial search agent (population) of the equilibrium optimizer algorithm is generated through opposition-based learning, and the particles (individuals in the population) of the search agent are updated using the IEO algorithm. Compared with the completely randomly generated search agent, the method of initializing the search agent through the IEO algorithm has a better convergence effect. Then, each segment of the search agent is mapped to the corresponding parameters of the RRVFL, and the effective input weight and hidden layer bias are selected according to the node activation to generate the network structure, which can realize the simultaneous optimization of hyperparameters and common parameters. Finally, by calculating the output weight, the light source color prediction of the image under unknown illumination is performed, and the image is corrected. Comparison experiments show that the IEO-RRVFL color constancy algorithm proposed in this paper has higher accuracy and better stability than other comparison algorithms.

Funder

Key R&D Program of Zhejiang Province

Natural Science Foundation of Zhejiang Province

Publisher

Optica Publishing Group

Subject

Computer Vision and Pattern Recognition,Atomic and Molecular Physics, and Optics,Electronic, Optical and Magnetic Materials

Reference41 articles.

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