Assessment of the possibility of imitating experts' aesthetic judgments about the impact of knots on the attractiveness of furniture fronts made of pine wood

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Krzysztof Gajowniczek
Marcin Bator
Katarzyna Śmietańska
Jarosław Górski

Keywords : image processing, knots on the fronts, machine learning, preference learning, solid wood furniture, quality control, variable's importance

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.

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Gajowniczek, K., Bator, M., Śmietańska, K., & Górski, J. (2023). Assessment of the possibility of imitating experts’ aesthetic judgments about the impact of knots on the attractiveness of furniture fronts made of pine wood. Machine Graphics and Vision, 32(2), 67–88.

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