Assessment of the Possibility of Imitating Experts’ Aesthetic Judgments about the Impact of Knots on the Beauty of Furniture Fronts Made of Pine Wood

• Autorzy: Gajowniczek Krzysztof , Bator Marcin , Śmietańska Katarzyna , Górski Jarosław

Identyfikatory

DOI

10.22630/MGV.2023.32.2.4

• Języki publikacji: EN

Abstrakty

EN

Our research aims to reconstruct expert preferences regarding the visual attractiveness of furniture fronts made of pine wood using machine learning algorithms. A numerical experiment was performed using five machine learning algorithms of various paradigms. To find the answer to the question of what determines the expert’s decision, we determined the importance of variables for some machine learning models. For random forest and classification trees, it involves the overall reduction in node impurities resulting from variable splitting, while for neural networks it uses the Garson algorithm. Based on the numerical experiments we can conclude that the best results of expert decision reconstruction are provided by a neural network model. The expert’s decision is better reconstructed for more beautiful images. The decision for nice images is made based on the best 4 or 5 variables, while for ugly images many more features are important. Prettier images and those for which the expert’s decision is better reconstructed have fewer knots.

Słowa kluczowe

EN

image processing; knots on the fronts; machine learning; preference learning; solid wood furniture; quality control; importance of variables

• Wydawca: Institute of Information Technology of the Warsaw University of Life Sciences – SGGW
• Czasopismo: Machine Graphics and Vision
• Rocznik: 2023
• Tom: Vol. 32, No. 2
• Strony: 67--88
• Opis fizyczny: Bibliogr. 37 poz., il., rys., tab.

Twórcy

autor

Gajowniczek Krzysztof

• Institute of Information Technology, Warsaw University of Life Sciences - SGGW, Warsaw, Poland

• https://orcid.org/0000-0001-6953-8907

autor

Bator Marcin

• Institute of Information Technology, Warsaw University of Life Sciences - SGGW, Warsaw, Poland

• https://orcid.org/0000-0002-6881-3695

autor

Śmietańska Katarzyna

• nstitute of Wood Sciences and Furniture, Warsaw University of Life Sciences - SGGW, Warsaw, Poland

• https://orcid.org/0000-0001-8705-3700

autor

Górski Jarosław

• Institute of Wood Sciences and Furniture, Warsaw University of Life Sciences - SGGW, Warsaw, Poland

• https://orcid.org/0000-0001-5615-0735

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).