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Modeling the Ink Tuning Process Using Machine Learning

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Intelligent Data Engineering and Automated Learning – IDEAL 2023 (IDEAL 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14404))

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Abstract

Paint bases are the essence of the color palette, allowing for the creation of a wide range of tones by combining them in different proportions. In this paper, an Artificial Neural Network is developed incorporating a pre-trained Decoder to predict the proportion of each paint base in an ink mixture in order to achieve the desired color. Color coordinates in the CIELAB space and the final finish are considered as input parameters. The proposed model is compared with commonly used models such as Linear Regression, Random Forest and Artificial Neural Network. It is important to note that the Artificial Neural Network was implemented with the same architecture as the proposed model but without incorporating the pre-trained Decoder. Experimental results demonstrate that the Artificial Neural Network with a pre-trained Decoder consistently outperforms the other models in predicting the proportions of paint bases for color tuning. This model exhibits lower Mean Absolute Error and Root Mean Square Error values across multiple objectives, indicating its superior accuracy in capturing the complexities of color relationships.

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Acknowledgements

This work is financed by National Funds through the Portuguese funding agency, Fundação para a Ciência e a Tecnologia (FCT), within project LA/P/0063/2020.

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Authors and Affiliations

  1. Institute of Engineering, Polytechnic Institute of Porto - ISEP/IPP, Porto, Portugal

    Catarina Costa & Carlos Abreu Ferreira

  2. INESC TEC, Porto, Portugal

    Carlos Abreu Ferreira

Authors
  1. Catarina Costa
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  2. Carlos Abreu Ferreira
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Corresponding authors

Correspondence to Catarina Costa or Carlos Abreu Ferreira .

Editor information

Editors and Affiliations

  1. University of Évora, Évora, Portugal

    Paulo Quaresma

  2. Technical University of Madrid, Madrid, Spain

    David Camacho

  3. University of Manchester, Manchester, UK

    Hujun Yin

  4. University of Évora, Évora, Portugal

    Teresa Gonçalves

  5. Polytechnic University of Valencia, Valencia, Spain

    Vicente Julian

  6. University of Huelva, Huelva, Spain

    Antonio J. Tallón-Ballesteros

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Costa, C., Ferreira, C.A. (2023). Modeling the Ink Tuning Process Using Machine Learning. In: Quaresma, P., Camacho, D., Yin, H., Gonçalves, T., Julian, V., Tallón-Ballesteros, A.J. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2023. IDEAL 2023. Lecture Notes in Computer Science, vol 14404. Springer, Cham. https://doi.org/10.1007/978-3-031-48232-8_36

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  • DOI: https://doi.org/10.1007/978-3-031-48232-8_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-48231-1

  • Online ISBN: 978-3-031-48232-8

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