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.
Highlights
The original spectral reflectance is projected from spectral space to characteristic variance space. It is calculated the relative distance difference by using the JS divergence method in the feature space of variance, that between the eigenvalue spectral information. It can solve the same pigment under the condition of the different spectral curve of amplitude difference smaller and reflectance curve is close to the problem that is difficult to identify, the matching similarity value of the different pigment samples can be calculated quickly
Studying the spectral matching algorithms of mural pigments is of great significance in the protection and research of cultural relics
Compared with the similarity values calculated by the traditional Spectral Angel Mapping (SAM), Spectral Correlation Fitting (SCF), and Spectral Information Divergence (SID) algorithms, the multi-state similar spectral matching classification calculation method based on feature differences substantially improves calculation accuracy
Summary
The pigment of protection and restoration are essential content of ancient mural protection. The original spectral reflectance is projected from spectral space to characteristic variance space It is calculated the relative distance difference by using the JS divergence method in the feature space of variance, that between the eigenvalue spectral information. It can solve the same pigment under the condition of the different spectral curve of amplitude difference smaller and reflectance curve is close to the problem that is difficult to identify, the matching similarity value of the different pigment samples can be calculated quickly
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