Optimized clustering method for spectral reflectance recovery

Xiong, Yifan; Wu, Guangyuan; Li, Xiaozhou; Wang, Xin

doi:10.3389/fpsyg.2022.1051286

Cited by 6 publications

(2 citation statements)

References 29 publications

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“…Zhang [ 32 ] selected testing samples by distance, and Liang [ 1 ] used the nearest training sample as the training subspace, which are examples of dynamic partitioning. Xiong [ 33 ] used dynamic partitional clustering to recover the spectral reflectance from camera response values.…”

Section: Introductionmentioning

confidence: 99%

Optimized Method Based on Subspace Merging for Spectral Reflectance Recovery

Xiong

2023

Sensors

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The similarity between samples is an important factor for spectral reflectance recovery. The current way of selecting samples after dividing dataset does not take subspace merging into account. An optimized method based on subspace merging for spectral recovery is proposed from single RGB trichromatic values in this paper. Each training sample is equivalent to a separate subspace, and the subspaces are merged according to the Euclidean distance. The merged center point for each subspace is obtained through many iterations, and subspace tracking is used to determine the subspace where each testing sample is located for spectral recovery. After obtaining the center points, these center points are not the actual points in the training samples. The nearest distance principle is used to replace the center points with the point in the training samples, which is the process of representative sample selection. Finally, these representative samples are used for spectral recovery. The effectiveness of the proposed method is tested by comparing it with the existing methods under different illuminants and cameras. Through the experiments, the results show that the proposed method not only shows good results in terms of spectral and colorimetric accuracy, but also in the selection representative samples.

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Section: Introductionmentioning

confidence: 99%

Optimized Method Based on Subspace Merging for Spectral Reflectance Recovery

Xiong

2023

Sensors

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“…There are two ways to quantitatively represent the color of an object: three colorimetric values (tristimulus value) and spectral color information [1]. Due to the influence of light source and observer, the use of tristimulus values to represent color has the phenomenon of 'metameric issues' [2]. Spectral reflectance is an inherent property of an object that is not affected by external conditions such as light source and * Author to whom any correspondence should be addressed.…”

Section: Introductionmentioning

confidence: 99%

Optimized clustering sample selection for spectral reflectance recovery

Wu,

Niu,

Xiong

2023

Laser Phys. Lett.

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The accuracy of spectral recovery depends heavily on the selection of an appropriate sample set, so the optimized sample selection by clustering strategy can improve the spectral recovery results. This paper presents a sample optimization method that combines hierarchical clustering and K-mean angle similar clustering to achieve this process. The proposed method employs the hierarchical clustering to divide the training sample dataset into 15 subspaces and obtain 15 subspace centroids. The similarity distance is then calculated between the testing sample and each subspace samples, and the subspace with the sample having the smallest distance is selected. The testing sample is utilized as a priori centroid, which clusters the optimal subspace by competition with the centroid of the subspace selected. This iterative process continues until the centroid of the subspace remains unaltered. Finally, the training samples within the optimal subspace use to recover spectral reflectance through Euclidean distance weighting. Experimental results demonstrate that the proposed method outperforms existing methods in terms of spectral and colorimetric accuracy, as well as stability and robustness. This research provides a solution to the problem of data redundancy in the spectral recovery process and enhances the accuracy and efficiency of spectral recovery.

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Optimizing Light Source Spectra for Art Conservation: Exploring Basic Color Groups

Kore,

Durmus

2024

LEUKOS

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Optimized clustering method for spectral reflectance recovery

Cited by 6 publications

References 29 publications

Optimized Method Based on Subspace Merging for Spectral Reflectance Recovery

Optimized Method Based on Subspace Merging for Spectral Reflectance Recovery

Optimized clustering sample selection for spectral reflectance recovery

Optimizing Light Source Spectra for Art Conservation: Exploring Basic Color Groups

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