Abstract
In product design, the impressions evoked by surface properties of objects attract attention. These impressions are called affective texture and demand is growing for technologies that quantify, index, and model it. Even in the fashion industry, the diversification of user needs necessitates the customization and personalization of products. Consequently, there has been a focus on custom-made services. However, enormous amounts of time and human costs are needed to find clothing patterns to suit one’s own preferences and ideas from among the countless patterns available. This research focused on affective texture related to visual impressions, and we proposed a method for automatically estimating the affective texture evoked by clothing patterns. To this end, we conducted the following steps: (1) quantified the visual impressions for patterns; (2) extracted style features as physical characteristics; and (3) modeled the relationships between visual impressions and physical characteristics. Afterward, based on the obtained models, we estimated the impressions for unlabeled patterns. Then, we verified their validity through relative evaluation and absolute evaluation, and we confirmed that our models estimated the impressions corresponded to the impressions that people actually felt. In addition, we implemented a system to enable users to intuitively search for patterns.
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References
Julesz, B.: Textons, the elements of texture perception, and their interactions. Nature 290(5802), 91 (1981)
Portilla, J., Simoncelli, E.P.: A parametric texture model based on joint statistics of complex wavelet coefficients. Int. J. Comput. Vis. 40(1), 49–70 (2000). https://doi.org/10.1023/A:1026553619983
Gatys, L.A., Ecker, A.S., Bethge, M: Image Style Transfer Using Convolutional Neural Networks. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2414–2423 (2016)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv preprint arXiv:1409.1556
Tuceryan, M.A., Jain, A.K.: Handbook of Pattern Recognition and Computer Vision. Texture Analysis, pp. 235–276. World Scientific (1993)
Takemoto, A., Tobitani, K., Tani, Y., Fujiwara, T., Yamazaki, Y., Nagata, N.: Texture synthesis with desired Visual impressions using deep correlation feature. In: IEEE International Conference on Consumer Electronics, pp. 739–740 (2019)
Tobitani, K., Matsumoto, T., Tani, Y., Fujii, H., Nagata, N.: Modeling of the relation between impression and physical characteristics on representation of skin surface quality. J. Inst. Image Inf. Telev. Eng. 71(11), 259–268 (2017)
Doizaki, R., Iiba, S., Okatani, T., Sakamoto, M.: Possibility to use product image and review text based on the association between onomatopoeia and texture. Trans. Jpn. Soc. Artif. Intell. 30(1), 57–60 (2015)
Mori, T., Uchida, Y., Komiyama, J.: Relationship between visual impressions and image information parameters of color textures. J. Jpn. Res. Assoc. Text. end-uses 51(5), 433–440 (2010)
Mouri, C., Ueda, E.S., Terauchi, F., Aoki, H.: Relationship between Mimetic words and kansei and sensory characteristics. Bull. Jpn. Soc. Sci. Des. 58, 209 (2011)
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Sunda, N., Tobitani, K., Tani, I., Tani, Y., Nagata, N., Morita, N. (2020). Impression Estimation Model for Clothing Patterns Using Neural Style Features. In: Stephanidis, C., Antona, M. (eds) HCI International 2020 - Posters. HCII 2020. Communications in Computer and Information Science, vol 1226. Springer, Cham. https://doi.org/10.1007/978-3-030-50732-9_88
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DOI: https://doi.org/10.1007/978-3-030-50732-9_88
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