Smart Media Journal (스마트미디어저널)

THE KOREAN INSTITUTE OF SMART MEDIA (한국스마트미디어학회)
권호 표지
DOI QR코드

DOI QR Code

Detection of Traditional Costumes: A Computer Vision Approach

  • Received : 2023.10.15
  • Accepted : 2023.11.24
  • Published : 2023.12.29
Download PDF
⟨ Previous Next ⟩

Abstract

Traditional attire has assumed a pivotal role within the contemporary fashion industry. The objective of this study is to construct a computer vision model tailored to the recognition of traditional costumes originating from five distinct countries, namely India, Korea, Japan, Tanzania, and Vietnam. Leveraging a dataset comprising 1,608 images, we proceeded to train the cutting-edge computer vision model YOLOv8. The model yielded an impressive overall mean average precision (MAP) of 96%. Notably, the Indian sari exhibited a remarkable MAP of 99%, the Tanzanian kitenge 98%, the Japanese kimono 92%, the Korean hanbok 89%, and the Vietnamese ao dai 83%. Furthermore, the model demonstrated a commendable overall box precision score of 94.7% and a recall rate of 84.3%. Within the realm of the fashion industry, this model possesses considerable utility for trend projection and the facilitation of personalized recommendation systems.

Keywords

Acknowledgement

This research was supported by a grant from Korea Institute for Advancement of Technology (No, P0014711).

References

  1. Han, Xintong, Zuxuan Wu, Zhe Wu, Ruichi Yu, and Larry S. Davis, "Viton: An image-based virtual try-on network," In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7543-7552, 2018. 
  2. Hadi Kiapour, M., Xufeng Han, Svetlana Lazebnik, Alexander C. Berg, and Tamara L. Berg, "Where to buy it: Matching street clothing photos in online shops," In Proceedings of the IEEE international conference on computer vision, pp. 3343-3351, 2015. 
  3. Huang, Junshi, Rogerio S. Feris, Qiang Chen, and Shuicheng Yan, "Cross-domain image retrieval with a dual attribute-aware ranking network," In Proceedings of the IEEE international conference on computer vision, pp. 1062-1070, 2015. 
  4. Liu, Ziwei, Ping Luo, Shi Qiu, Xiaogang Wang, and Xiaoou Tang, "Deepfashion: Powering robust clothes recognition and retrieval with rich annotations," In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1096-1104, 2016. 
  5. Ren, Shuyun, Hau-Ling Chan, and Tana Siqin, "Demand forecasting in retail operations for fashionable products: methods, practices, and real case study," Annals of Operations Research, vol. 291, pp. 761-777, 2020.  https://doi.org/10.1007/s10479-019-03148-8
  6. Audita, Handini, Bernardo Figueiredo, and Lauren Gurrieri, "The fashion development process for traditional costumes in the contemporary global fashion market," Journal of Global Fashion Marketing, vol. 14, no. 1, pp. 108-122,
  7. Liu, Yongru, "Application of Jiangxi intangible cultural heritage in modern fashion design," In 2017 3rd International Conference on Economics, Social Science, Arts, Education and Management Engineering (ESSAEME 2017), Atlantis Press, 2017. 
  8. Tajuddin, Fatjri Nur, "Cultural and social identity in clothing matters "different cultures, different meanings," European Journal of Behavioral Sciences, vol. 1, no. 4, pp. 21-25, 2018.  https://doi.org/10.33422/EJBS.2018.07.63
  9. Atomon, F, The Significance of Traditional Clothing Across Cultures https://www.alittlelouder.co/cultureinhues/traditional-clothing-across-cultures, (accessed Feb., 20, 2023). 
  10. Terven J., Cordova-Esparza D. A Comprehensive Review of YOLO: From YOLOv1 to YOLOv8 and Beyond, 2023. arXiv preprint arXiv:2304.00501  https://doi.org/10.3390/make5040083
  11. Cheng, Wen-Huang, Song, S., Chen, C. Y., Hidayati, S. C. and Jiaying Liu, "Fashion meets computer vision: A survey," ACM Computing Surveys (CSUR), pp. 1-41, 2021. 
  12. Piacentini, Maria, and Greig Mailer, "Symbolic consumption in teenagers' clothing choices," Journal of Consumer Behaviour: An International Research Review, vol. 3, no. 3, pp. 251-262, 2004.  https://doi.org/10.1002/cb.138
  13. Yamaguchi, Kota, M. Hadi Kiapour, and Tamara L. Berg, "Paper doll parsing: Retrieving similar styles to parse clothing items," Proceedings of the IEEE international conference on computer vision, 2013. 
  14. Yamaguchi, Kota, M. Hadi Kiapour, Luis E. Ortiz, and Tamara L. Berg, "Retrieving similar styles to parse clothing," IEEE transactions on pattern analysis and machine intelligence, vol. 37, no. 5, pp. 1028-1040, 2014.  https://doi.org/10.1109/TPAMI.2014.2353624
  15. Xu, Qi, Hubin Liu, Yulong Liu, and Shan Wu, "Innovative design of intangible cultural heritage elements in fashion design based on interactive evolutionary computation," Mathematical Problems in Engineering 2021, pp. 1-11, 2021.  https://doi.org/10.1155/2021/9913161
  16. Lee, Myung-Joon, and Hyon-Sook Choy, "A study on contemporary fashion design with the application of Korean traditional embroidery I," Journal of the Korean Society of Costume, vol. 57, no. 3, pp. 176-190, 2007. 
  17. Staynov, Stefan, Kristian Milev, Georgi Kostadinov, and Asya Stoyanova-Doycheva. "Traditional Bulgarian Costume Classification in a Tourist Guide application," In 2020 International Conference Automatics and Informatics (ICAI), pp. 1-4, 2020. 
  18. Liu, Kaixuan, Kai Lin, and Chun Zhu. "Research on Chinese traditional opera costume recognition based on improved YOLOv5," Heritage Science, vol. 11, no. 1, pp. 1-14, 2023.  https://doi.org/10.1186/s40494-023-00883-x
  19. Jiang, Peiyuan, Daji Ergu, Fangyao Liu, Ying Cai, and Bo Ma, "A Review of Yolo algorithm developments," Procedia Computer Science, vol. 199, pp. 1066-1073, 2022.  https://doi.org/10.1016/j.procs.2022.01.135
  20. Redmon, Joseph, Santosh Divvala, Ross Girshick, and Ali Farhadi, "You only look once: Unified, real-time object detection," In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 779-788, 2016. 
  21. Ultralytics, Ultralytics YOLOv8 documentation https://docs.ultralytics.com/ (accessed Feb., 20, 2023). 
  22. Jacob S, Francesco, what is YOLOv8? The Ultimate Guide, https://blog.roboflow.com/whats-new-in-yolov8/ (accessed Feb., 20, 2023). 
  23. Padilla, Rafael, Sergio L. Netto, and Eduardo AB Da Silva, "A survey on performance metrics for object-detection algorithms," 2020 international conference on systems, signals and image processing (IWSSIP). IEEE, 2020.