Identification of Pure Painting Pigment Using Machine Learning Algorithms

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationArtificial Intelligence in Music, Sound, Art and Design - 10th International Conference, EvoMUSART 2021, Held as Part of EvoStar 2021, Proceedings
EditorsJuan Romero, Tiago Martins, Nereida Rodríguez-Fernández
Place of PublicationCham
PublisherSpringer
Pages52-64
Number of pages13
ISBN (Electronic)978-3-030-72113-8
ISBN (Print)978-3-030-72112-1
DOIs
Publication statusPublished - 2021
Event10th International Conference on Artificial Intelligence in Music, Sound, Art and Design, EvoMUSART 2021 held as Part of EvoStar 2021 - Virtual, Online
Duration: 7 Apr 20219 Apr 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer
Volume12693 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th International Conference on Artificial Intelligence in Music, Sound, Art and Design, EvoMUSART 2021 held as Part of EvoStar 2021
CityVirtual, Online
Period7/04/219/04/21

Keywords

  • Artificial intelligence
  • Hyperspectral imaging
  • Machine learning
  • Neural network
  • Painting reconstruction
  • Pigment identification
  • Pigment unmixing
  • Restoration
  • Visualization

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