Open Access

ARTICLE

Spectral Matching Classification Method of Multi-State Similar Pigments Based on Feature Differences

Meng Da¹, Huiqin Wang^1,*, Ke Wang¹, Zhan Wang²
1 Xi'an University of Architecture and Technology, Xi'an, 710055, China
2 Shaanxi Provincial Institute of Cultural Relics Protection, Xi'an, 710075, China
* Corresponding Author: Huiqin Wang. Email:
(This article belongs to this Special Issue: Computer Modeling for Smart Cities Applications)

Computer Modeling in Engineering & Sciences 2022, 131(1), 513-527. https://doi.org/10.32604/cmes.2022.019040

Received 31 August 2021; Accepted 13 October 2021; Issue published 24 January 2022

Abstract

The properties of the same pigments in murals are affected by different concentrations and particle diameters, which cause the shape of the spectral reflectance data curve to vary, thus influencing the outcome of matching calculations. This paper proposes a spectral matching classification method of multi-state similar pigments based on feature differences. Fast principal component analysis (FPCA) was used to calculate the eigenvalue variance of pigment spectral reflectance, then applied to the original reflectance values for parameter characterization. We first projected the original spectral reflectance from the spectral space to the characteristic variance space to identify the spectral curve. Secondly, the relative distance between the eigenvalues in the eigen variance space is combined with the JS (Jensen-Shannon) divergence to express the difference between the two spectral distributions. The JS information divergence calculates the relative distance between the eigenvalues. Experimental results show that our classification method can be used to identify the spectral curves of the same pigment under different states. The value of the root means square error (RMSE) decreased by 12.0817, while the mean values of the mean absolute percentage error (MAPE) and R2 increased by 0.0965 and 0.2849, respectively. Compared with the traditional spectral matching algorithm, the recognition error was effectively reduced.

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Keywords

Fast principal component analysis; paint samples; reflectivity; information divergence; feature variance

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