Industrial Applications of Affective Engineering pp 111-122 | Cite as
Modeling Emotional Evaluation of Traditional Vietnamese Aodai Clothes Based on Computer Vision and Machine Learning
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Abstract
The more that human society develops, the greater the human need for well-mannered and elegant clothes, especially traditional costumes. Selecting fine clothes for a specific occasion is always an interesting individual question. Based on computer vision and machine learning, this research proposes a Kansei (emotional) evaluation for Aodai, which is traditional and well-known Vietnamese clothes for women. Features of an Aodai image are described by color coherence vectors. Self-organizing maps (SOMs) and multilayer neural networks (NNs) are used to learn the relationships between the image features and the Kansei words. Once learned, the system can recommend which Aodai is suitable for a woman through her desired feelings. She can use this recommendation when purchasing an Aodai at online stores or selecting one from her own collection for an outing. Topics for future research include investigating other image representation methods, such as combinations of color buckets in different parts of the Aodai, using more detailed descriptions in decorative patterns, and integrating conspicuity factors such as color harmony, discriminability and visibility.
Keywords
Kansei Vietnamese Aodai Fashion design Traditional costume Color coherence vectorNotes
Acknowledgments
The authors would like to thank Mr. Dang Tuan Linh at Ritsumeikan University and other people for their valuable help on the Aodai evaluation survey.
References
- 1.Kauffner P (2010) Aodai: the allure and grace of Vietnam’s traditional dress, Asia insights: destination AsiaGoogle Scholar
- 2.Nagamachi M (2010) Kansei engineering: Kansei/affective engineering (industrial innovation), 1st edn. CRC Press, Boca RatonGoogle Scholar
- 3.Ogata Y, Onisawa T (2008) Interactive clothes design support system. Lect Notes Comput Sci 4985:657–665CrossRefGoogle Scholar
- 4.Kim H-S, Cho S-B (2000) Application of interactive genetic algorithm to fashion design. Eng Appl Artif Intell 13:635–644CrossRefGoogle Scholar
- 5.Santos M, Rebelo F (2007) An expert system to support clothing design process. Lect Notes Comput Sci 4566:284–289CrossRefGoogle Scholar
- 6.Anitawati ML, Laila N, Nagamachi M (2007) Kansei engineering: a study on perception of online clothing website. In: Proceedings of the 10th international conference on quality management and operation developmentGoogle Scholar
- 7.Chang Y-C et al (2003) A Kansei study on the style image of fashion design. In: 6th Asian design international conferenceGoogle Scholar
- 8.Ishihara S, Ishihara K, Nagamachi M, Matsubara Y (1997) An analysis of Kansei structure on shoes using self-organizing neural networks. Int J Ind Ergon 19:93–104CrossRefGoogle Scholar
- 9.Cooper EW, Kamei K (2002) A study of color conspicuity for ease-of-use inference in visualization. Color Res Appl 27(2):74–82CrossRefGoogle Scholar
- 10.Pass G, Zabih R, Miller J (1997) Comparing images using color coherence vectors. In: Proceedings of the 4th ACM international conference on multimedia, pp 65–73Google Scholar
- 11.Kohonen T (2006) Self-organizing maps, 3rd edn. Springer New YorkGoogle Scholar
- 12.Cao T, Kamei K, Dang TL (2009) Visualization system of herbal prescription effects in oriental medicine by self-organizing map. Biomed Soft Comput Hum Sci 14(1):101–108Google Scholar
- 13.The CS, Lim Chee Peng (2007) A hybrid Kansei engineering system using the self-organizing map neural network. J IT Asia 2(1):23–38Google Scholar
- 14.Thang C et al (2006) A proposed model of diagnosis and prescription in oriental medicine using RBF neural networks. J Adv Comput Intell Intell Inf 10(4):458–464MathSciNetGoogle Scholar
- 15.Kinoshita Y et al (2006) Kansei and colour harmony models for townscape evaluation. J Syst Control Eng 220(8):725–734Google Scholar
- 16.Cao T, Hoshino Y (2013) A proposal of Kansei evaluation for traditional vietnamese Aodai clothes based on computer vision. In: Proceedings of 1st international symposium on affective engineering (ISAE2013), pp 31–36Google Scholar