Abstract
Can machines learn to perceive products like humans? Kansei Engineering has been developed to connect product design and human perception. While conventional Kansei Engineering Systems exhibit a high dependency on manual extraction of design elements and thereby are restricted to validity issues, we present a Pixel-level Image Kansei Analysis and Recognition (PIKAR) system that applies deep learning to extract and analyze the formation of human perception towards product designs automatically. Method validation is performed based on the evaluation of cosmetic packaging’s kawaii. Two neural nets trained on 1,414 images, labeled by eight participants based on their perception of kawaii (1–5 Likert Scale), have achieved a better prediction than test persons. The implemented neuron analysis methodology for Kansei analysis points towards consistency with previous experimental kawaii studies and gives insight to individual differences. This work addresses the possibility of applying deep learning to support product design and user experience researches.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Nagamachi, M.: Kansei engineering: a new ergonomic consumer-oriented technology for product development. Int. J. Ind. Ergon. 15, 3–11 (1995)
Matsubara, Y., Nagamachi, M.: Hybrid Kansei engineering system and design support. Int. J. Ind. Ergon. 19, 81–92 (1997)
Tsuchiya, T., Ishihara, S., Matsubara, Y., Nishino, T., Nagamachi, M.: A method for learning decision tree using genetic algorithm and its application to Kansei engineering system. In: IEEE SMC 1999 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No. 99CH37028), pp. 279–283. IEEE (1999)
Nagamachi, M., Tachikawa, M., Imanishi, N., Ishizawa, T., Yano, S.: A successful statistical procedure on kansei engineering products. In: 11th QMOD Conference. Quality Management and Organizational Development Attaining Sustainability from Organizational Excellence to SustainAble Excellence, Helsingborg, Sweden, 20–22 August 2008, pp. 987–995. Linköping University Electronic Press (2008)
Chen, J.-S., Wang, K.-C., Liang, J.-C.: A hybrid kansei design expert system using artificial intelligence. In: Ho, T.-B., Zhou, Z.-H. (eds.) PRICAI 2008. LNCS (LNAI), vol. 5351, pp. 971–976. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-89197-0_93
Pritchard, S.C., Zopf, R., Polito, V., Kaplan, D.M., Williams, M.A.: Non-hierarchical influence of visual form, touch, and position cues on embodiment, agency, and presence in virtual reality. Front Psychol. 7, 1649 (2016). https://doi.org/10.3389/fpsyg.2016.01649
Wang, K.-C.: A hybrid Kansei engineering design expert system based on grey system theory and support vector regression. Expert Syst. Appl. 38, 8738–8750 (2011)
Gong, Y., Rau, P.-L.P.: DL-KES: a deep learning based kansei engineering system (特集 人工知能と感性). 感性工学 = J. Jpn Soc. Kansei Eng. 15, 29–31 (2017)
Ohkura, M., Konuma, A., Murai, S., Aoto, T.: Systematic study for “kawaii” products (the second report)-commpmrison of “kawaii” colors and shapes. In: 2008 SICE Annual Conference, pp. 481–484. IEEE (2008)
Ohkura, M., Komatsu, T., Aoto, T.: Kawaii rules: increasing affective value of industrial products. In: Watada, J., Shiizuka, H., Lee, K.-P., Otani, T., Lim, C.-P. (eds.) Industrial Applications of Affective Engineering, pp. 97–110. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-04798-0_8
Nomura, J., Imamura, K., Enomoto, N., Nagamachi, M.: Virtual space decision support system using Kansei engineering. In: Kunii, T.L., Luciani, A. (eds.) Cyberworlds, pp. 273–288. Springer, Tokyo (1998). https://doi.org/10.1007/978-4-431-67941-7_18
Chen, T., Borth, D., Darrell, T., Chang, S.-F.: Deepsentibank: visual sentiment concept classification with deep convolutional neural networks. arXiv preprint arXiv:1410.8586 (2014)
Lu, X., Lin, Z., Jin, H., Yang, J., Wang, J.Z.: Rapid: rating pictorial aesthetics using deep learning. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 457–466 (2014)
Khosla, A., Das Sarma, A., Hamid, R.: What makes an image popular? In: Proceedings of the 23rd International Conference on World Wide Web, pp. 867–876 (2014)
Isola, P., Xiao, J., Torralba, A., Oliva, A.: What makes an image memorable? In: CVPR 2011, pp. 145–152. IEEE (2011)
Karayev, S., et al.: Recognizing image style. arXiv preprint arXiv:1311.3715 (2013)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)
Yosinski, J., Clune, J., Nguyen, A., Fuchs, T., Lipson, H.: Understanding neural networks through deep visualization. arXiv preprint arXiv:1506.06579 (2015)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Jia, Y., et al.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 675–678 (2014)
Cheok, A.D.: Kawaii/cute interactive media. In: Cheok, A.D. (ed.) Art and Technology of Entertainment Computing and Communication, pp. 223–254. Springer, London (2010). https://doi.org/10.1007/978-1-84996-137-0_9
Acknowledgement
This work was funded by Tsinghua University Initiative Scientific Research Program 20193080010.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Gong, Y., Wang, B., Rau, PL.P. (2020). PIKAR: A Pixel-Level Image Kansei Analysis and Recognition System Based on Deep Learning for User-Centered Product Design. In: Rau, PL. (eds) Cross-Cultural Design. User Experience of Products, Services, and Intelligent Environments. HCII 2020. Lecture Notes in Computer Science(), vol 12192. Springer, Cham. https://doi.org/10.1007/978-3-030-49788-0_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-49788-0_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-49787-3
Online ISBN: 978-3-030-49788-0
eBook Packages: Computer ScienceComputer Science (R0)