TY - GEN
T1 - Identification of Pure Painting Pigment Using Machine Learning Algorithms
AU - Chen, Ailin
AU - Jesus, Rui
AU - Vilarigues, Márcia
N1 - Funding Information:
info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04516%2F2020/PT#
info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00729%2F2020/PT#
info:eu-repo/grantAgreement/FCT/OE/PD%2FBD%2F135223%2F2017/PT#
Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - This paper reports the implementation of machine learning techniques in the identification of pure painting pigments applying spectral data obtained from both the paint tubes used and the paintings produced by Portuguese artist Amadeo de Souza Cardoso. It illustrates the rationales and advantages behind the application of more accurate artificial mixing by subtractive mixing on the reference pigments as well as the use of Root Mean Square Error (RMSE) for distinguishing especially the mixtures that contain white and black, so that a more holistic machine learning approach can be applied; notably, the experiment of neural network for discerning black and white pigments, which later could be applied for both pure and mixed pigment identification. Other machine learning techniques like Decision Tree and Support Vector Machine are also exploited and compared in terms of the identification of pure pigments. In addition, this paper proposes the solution to the common problem of highly-imbalanced and limited data in the analysis of historical artwork field.
AB - This paper reports the implementation of machine learning techniques in the identification of pure painting pigments applying spectral data obtained from both the paint tubes used and the paintings produced by Portuguese artist Amadeo de Souza Cardoso. It illustrates the rationales and advantages behind the application of more accurate artificial mixing by subtractive mixing on the reference pigments as well as the use of Root Mean Square Error (RMSE) for distinguishing especially the mixtures that contain white and black, so that a more holistic machine learning approach can be applied; notably, the experiment of neural network for discerning black and white pigments, which later could be applied for both pure and mixed pigment identification. Other machine learning techniques like Decision Tree and Support Vector Machine are also exploited and compared in terms of the identification of pure pigments. In addition, this paper proposes the solution to the common problem of highly-imbalanced and limited data in the analysis of historical artwork field.
KW - Artificial intelligence
KW - Hyperspectral imaging
KW - Machine learning
KW - Neural network
KW - Painting reconstruction
KW - Pigment identification
KW - Pigment unmixing
KW - Restoration
KW - Visualization
UR - http://www.scopus.com/inward/record.url?scp=85107456652&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-72914-1_4
DO - 10.1007/978-3-030-72914-1_4
M3 - Conference contribution
AN - SCOPUS:85107456652
SN - 978-3-030-72112-1
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 52
EP - 64
BT - Artificial Intelligence in Music, Sound, Art and Design - 10th International Conference, EvoMUSART 2021, Held as Part of EvoStar 2021, Proceedings
A2 - Romero, Juan
A2 - Martins, Tiago
A2 - Rodríguez-Fernández, Nereida
PB - Springer
CY - Cham
T2 - 10th International Conference on Artificial Intelligence in Music, Sound, Art and Design, EvoMUSART 2021 held as Part of EvoStar 2021
Y2 - 7 April 2021 through 9 April 2021
ER -