Combinational illumination estimation method based on image-specific PCA filters and support vector regression

Šavc, Martin; Potočnik, Božidar

doi:10.1007/s00138-017-0860-4

Cited by 6 publications

(4 citation statements)

References 15 publications

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“…[18][19][20][21] If an elaborate ensemble framework is designed, it is possible to obtain promising performance from these combinational methods. 19,[22][23][24] Considering that training datasets are commonly small-scale in the CC field, efficient model ensembles of simple algorithms are also appealing to researchers.…”

Section: Introductionmentioning

confidence: 99%

“…For trade-offs, there is a small class of methods that integrate multiple unitary algorithms, either statistics-based or learning-based, to estimate illumination colors with low model complexity and computational cost 18 – 21 If an elaborate ensemble framework is designed, it is possible to obtain promising performance from these combinational methods 19 , 22 – 24 Considering that training datasets are commonly small-scale in the CC field, efficient model ensembles of simple algorithms are also appealing to researchers.…”

Section: Introductionmentioning

confidence: 99%

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LSSVM-CC: ensemble algorithm of color constancy using least square support vector machine

Wang,

Luo,

Wang

et al. 2023

J. Electron. Imag.

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Color constancy (CC) refers to the ability to reproduce the veritable color of objects by eliminating the influence of environmental illumination. The performance of CC has been greatly boosted by advanced learning-based methods, such as deep neural networks. However, most current deep learning methods suffer from large model sizes and large-scale training datasets for promising performance. We propose least square support vector machine-CC (LSSVM-CC), an efficient CC method using LSSVM, which combines preferably unitary statistics-based methods to achieve better illuminant estimation performance at low computation efforts with small training samples. The core of our LSSVM-CC is to cluster training images based on illumination-preferred features and then train an ensemble model using LSSVM for each cluster, which can effectively improve the overall performance. Once trained, our LSSVM-CC selects adaptive models for different input images to perform accurate illuminant estimation in the testing phase. Experiments on typical benchmark datasets show that our method significantly outperforms most traditional statistics-based methods and is also comparable to some sophisticated and computationally demanding learning-based methods.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

LSSVM-CC: ensemble algorithm of color constancy using least square support vector machine

Wang,

Luo,

Wang

et al. 2023

J. Electron. Imag.

View full text Add to dashboard Cite

show abstract

“…The main research in this article focuses on color constancy algorithms under uniform illumination, furthermore, attaches more importance to the fusion methodology of various single algorithm. Under the controlled experimental conditions, the fusion method of SVR proved to be superior . Hence, this article adopts LS‐SVR, which is improved by SVR, as a new fusion method to replace those previous methods.…”

Section: Introductionmentioning

confidence: 99%

“…Under the controlled experimental conditions, the fusion method of SVR proved to be superior. [15][16][17] Hence, this article adopts LS-SVR, which is improved by SVR, as a new fusion method to replace those previous methods. Support vector regression, as a popular machine learning method, embodies the structural risk minimization principle and finds the optimal hyperplane in high dimensional feature space.…”

Section: Introductionmentioning

confidence: 99%

The sparse least square support vector regression for estimating illumination chromaticity

Zhu

Liu

Song

et al. 2018

Color Research & Application

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The fusion of estimating algorithms for illumination chromaticity is an important strategy in computational color constancy and it has attracted the much attention of domestic and foreign scholars. Some effective approaches have been proposed to build the framework of the fusion, such as Committee‐based Color Constancy, BP neural network, and machine learning regression. In this article, the framework of least square support vector regression (LS‐SVR) is applied to illumination estimation fusion and provides a novel sparse method of LS‐SVR. According to the experience of previous research, the complexity in training LS‐SVR is obviously lower than it in training SVR, but due to all of the characters in images are chosen as the support vectors, the computation in LS‐SVR takes more time. In order to improve accuracy and reduce time consumption, this study uses sparse strategy that only weighted samples that illumination chromaticity of image have higher weight than other images can be selected as the support vectors. Training via a part of the images with high‐weight, the sparse LS‐SVR can achieve a satisfactory result. Experiment with real images shows that this sparse LS‐SVR method performs better than SVR, LS‐SVR and current some other color constancy algorithms especially in the aspects of computing speed and accuracy.

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Illumination estimation via random vector functional link based on improved arithmetic optimization algorithm

Liu

Zhou

2021

Color Research & Application

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In order to solve the problem that the existing illumination estimation methods have low accuracy in image restoration, a random vector functional link illumination estimation algorithm based on whale optimization arithmetic algorithm is proposed in this article. First, in order to improve the optimization accuracy of arithmetic optimization algorithm, the whale optimization algorithm is applied to optimize the initial population of the arithmetic optimization algorithm. Then, we utilize the improved arithmetic optimization algorithm to improve the input weight and hidden layer offset of random vector functional link, and exclude the uncertainty caused by the random generation of random vector functional link's input weight and hidden layer bias. Finally, the proposed whale arithmetic optimization algorithm‐random vector functional link model is applied to SFU lab dataset and Gehler‐Shi dataset for illumination estimation. Compared with the other earlier illumination estimation algorithms, the proposed illumination estimation model in this article has better effect and higher stability. In addition, due to the diagonal mapping method for color correction and image restoration, the proposed illumination estimation algorithm can also get better results in image restoration.

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Combinational illumination estimation method based on image-specific PCA filters and support vector regression

Cited by 6 publications

References 15 publications

LSSVM-CC: ensemble algorithm of color constancy using least square support vector machine

LSSVM-CC: ensemble algorithm of color constancy using least square support vector machine

The sparse least square support vector regression for estimating illumination chromaticity

Illumination estimation via random vector functional link based on improved arithmetic optimization algorithm

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