Visual impression estimation system considering attribute information

Yukiya Taki; Kunihito Kato; Kazunori Terada; Kensuke Tobitani

doi:10.1117/12.2691716

28 July 2023 Visual impression estimation system considering attribute information

Yukiya Taki, Kunihito Kato, Kazunori Terada, Kensuke Tobitani

Proceedings Volume 12749, Sixteenth International Conference on Quality Control by Artificial Vision; 127490P (2023) https://doi.org/10.1117/12.2691716
Event: Sixteenth International Conference on Quality Control by Artificial Vision, 2023, Albi, France

Abstract

Detailed identification of visual impressions of objects by attributes can be leveraged to develop products and improve customer satisfaction. In this study, we propose a method to estimate Kansei (affective) information for each attribute, which is the visual impression received from the image. For each attribute, we created a dataset with Kansei indices. By fine-tuning the created dataset to combine attribute information with the output of ResNet18 which was already trained with ImageNet to output indexes, we confirmed that the correlation coefficients for multiple item ratings were higher than those of a deep learning model without attribute information.

Citation Download Citation

Yukiya Taki, Kunihito Kato, Kazunori Terada, and Kensuke Tobitani "Visual impression estimation system considering attribute information", Proc. SPIE 12749, Sixteenth International Conference on Quality Control by Artificial Vision, 127490P (28 July 2023); https://doi.org/10.1117/12.2691716

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