Pixel-based object recognition in fashion images to generate colour palettes

References

• Abdullayeva, A. (2019). Retrieved August 3, 2022. https://towardsdatascience.com/clothes-and-color-extraction-with-generative-adversarial-network-80ba117e17e6.
   Google Scholar
• Bell, J. (2022). What Is machine learning? Machine learning and the city: Applications in architecture and urban design. Hoboken: Wiley.
   Google Scholar
• Chakraborty, S., Hoque, S. A., & Kabir, S. F. (2020). Predicting fashion trend using runway images: Application of logistic regression in trend forecasting. International Journal of Fashion Design, Technology and Education, 13(3), 376–386. doi:10.1080/17543266.2020.1829096
   Google Scholar
• Colormind. (2022). Retrieved August 4, 2022. http://colormind.io/blog/.
   Google Scholar
• DuBreuil, M., & Lu, S. (2020). Traditional vs. big-data fashion trend forecasting: An examination using WGSN and EDITED. International Journal of Fashion Design, Technology and Education, 13(1), 68–77. doi:10.1080/17543266.2020.1732482
   Google Scholar
• Ge, Y., Zhang, R., Wang, X., Tang, X., & Luo, P. (2019). Deepfashion2: A versatile benchmark for detection, pose estimation, segmentation and re-identification of clothing images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 5337–5345).
   Google Scholar
• Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., & Bengio, Y. (2014). Generative adversarial nets. In: Proceedings of the International Conference on Neural Information Processing Systems (NIPS) (pp. 2672–2680).
   Google Scholar
• Gu, Z., Wu, Z., Yu, J., & Lou, J. (2013). A color schemer for webpage design using interactive mood board. In: International Conference on Human-Computer Interaction (pp. 555–564). Springer.
   Google Scholar
• Hong, H. R., & Kim, Y. I. (2019). A mobile application for personal colour analysis. Cogent Business & Management, 6(1).
   Web of Science ®Google Scholar
• Isola, P., Zhu, J. Y., Zhou, T., & Efros, A. A. (2017). Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1125–1134).
   Google Scholar
• Kittler, J., & Illingworth, J. (1986). Minimum error thresholding. Pattern Recognition, 19(1), 41–47. doi:10.1016/0031-3203(86)90030-0
   Web of Science ®Google Scholar
• Lai, P., & Westland, S. (2020). Machine learning for colour palette extraction from fashion runway images. International Journal of Fashion Design, Technology and Education, 13(3), 334–340. doi:10.1080/17543266.2020.1799080
   Google Scholar
• Liu, Z., Luo, P., Qiu, S., Wang, X., & Tang, X. (2016). 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).
   Google Scholar
• Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 22(2), 129–137. doi:10.1109/TIT.1982.1056489
   Google Scholar
• Luo, Y., Zheng, Z., Zheng, L., Guan, T., Yu, J., & Yang, Y. (2018). Macro-micro adversarial network for human parsing. In: Proceedings of the European Conference on Computer Vision (ECCV) (pp. 418–434).
   Google Scholar
• Merryman, L., & Lu, S. (2021). Are fashion majors ready for the era of data science? A study on the fashion undergraduate curriculums in US institutions. International Journal of Fashion Design, Technology and Education, 14(2), 139–150. doi:10.1080/17543266.2021.1884752
   Google Scholar
• Park, N., & Choi, Y. (2020). A form attribute classification system for fashion items. International Journal of Fashion Design, Technology and Education, 13(1), 11–19. doi:10.1080/17543266.2019.1664641
   Google Scholar
• Sayem, A. S. M. (2022). Digital fashion innovations for the real world and metaverse. International Journal of Fashion Design, Technology and Education, 15(2), 139–141. doi:10.1080/17543266.2022.2071139
   Google Scholar
• Sokolova, M., Japkowicz, N., & Szpakowicz, S. (2006). Beyond accuracy, F-score and ROC: a family of discriminant measures for performance evaluation. In: AI 2006: Advances in Artificial Intelligence: 19th Australian Joint Conference on Artificial Intelligence, Hobart, Australia, December 4-8, 2006. Proceedings 19 (pp. 1015–1021). Springer.
   Google Scholar
• Sun, F., Li, L., Qiu, X., & Liu, Y. (2018). U-net: Machine reading comprehension with unanswerable questions. arXiv preprint arXiv:1810.06638.
   Google Scholar
• VisualistApp. (2022). Retrieved July 7, 2022. https://www.visualistapp.com/.
   Google Scholar
• Westland, S., Ripamonti, C., & Cheung, V. (2012). Computational colour science using MATLAB. Hoboken: John Wiley & Sons.
   Google Scholar
• Yang, J., Chen, Y., Westland, S., & Xiao, K. (2020). Predicting visual similarity between colour palettes. Color Research & Application, 45(3), 401–408. doi:10.1002/col.22492
   Web of Science ®Google Scholar
• Zabotto, C. N., Amaral, D. C., Hornos, C. J., & Benze, B. G. (2019). Automatic digital mood boards to connect users and designers with Kansei engineering. International Journal of Industrial Ergonomics, 74, 102829. doi:10.1016/j.ergon.2019.102829
   Web of Science ®Google Scholar