Convolutional Neural Network-Based Pure Paint Pigment Identification Using Hyperspectral Images

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

Abstract

This research presents the results of the implementation of deep learning neural networks in the identification of pure pigments of heritage artwork, namely paintings. Our paper applies an innovative three-branch deep learning model to maximise the correct identification of pure pigments. The model proposed combines the feature maps obtained from hyperspectral images through multiple convolutional neural networks, and numerical, hyperspectral metric data with respect to a set of reference reflectances. The results obtained exhibit an accurate representation of the pure predicted pigments which are confirmed through the use of analytical techniques. The model presented outperformed the compared counterparts and is deemed to be an important direction, not only in terms of utilisation of hyperspectral data and concrete pigment data in heritage analysis, but also in the application of deep learning in other fields.

Original languageEnglish
Title of host publicationProceedings of the 3rd ACM International Conference on Multimedia in Asia, MMAsia 2021
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450386074
DOIs
Publication statusPublished - 1 Dec 2021
Event3rd ACM International Conference on Multimedia in Asia, MMAsia 2021 - Virtual, Online, Australia
Duration: 1 Dec 20213 Dec 2021

Publication series

NameACM International Conference Proceeding Series
PublisherAssociation for Computing Machinery

Conference

Conference3rd ACM International Conference on Multimedia in Asia, MMAsia 2021
Country/TerritoryAustralia
CityVirtual, Online
Period1/12/213/12/21

Keywords

  • convolutional neural networks
  • deep learning
  • hyperspectral imaging
  • pigment identification
  • visualisation

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