Implementing The Use of AI for Analysis and Prediction in the Fashion Industry
Abstract
The COVID-19 pandemic has made all aspects of human life assisted by technology and big data. It starts from the education sector, economy, communication, health, and manufacturing to fashion. As we all know fast fashion has become one of the most significant contributors of waste. During the flow of developing a collection, for example; the production and distribution process can cause ethical issues and contradict sustainability matters. Several studies from 2010 to date have initiated AI (Artificial Intelligent) technology, a computer vision that alleviates the use of carbon footprints in the fashion industry. AI presents robust evidence to the audience, since it is visual and statically calculated, furthermore it is less costly and energy saving. AI abstracts the similarities or differences across all clothing and collections from the dataset. Its implementation can be used in many fashion careers with different purposes. By reviewing across the computer vision journals complemented with fashion management literatures, this article eventually provides insights of the implementation of AI for analysis and prediction from fashion photos or dataset.
Full text article
References
Khakurel, J., Penzenstadler, B., Porras, J., Knutas, A., & Zhang, W. (2018). The rise of artificial intelligence under the lens of sustainability. Technologies, 6(4), 100. https://doi.org/10.3390/technologies6040100
Nishant, R., Kennedy, M., & Corbett, J. (2020). Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda. International Journal of Information Management, 53, 102104. https://doi.org/10.1016/j.ijinfomgt.2020.102104
Ayodele, T. O. (2010). Types of machine learning algorithms. New advances in machine learning. InTech.
Chan, H. L., Ren, S., & Liu, N. (2022). Overview and research agenda for sustainable operations management in fast-fashion era. Operations management in the era of fast fashion. Springer.
Luce, L. (2018). Artificial intelligence for fashion: how ai is revolutionizing the fashion industry. San Fransisco: Apress.
Al-Halah, Z., & Grauman, K. (2020). From paris to berlin: Discovering fashion style influences around the world. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 10136-10145). Cornell University. https://arxiv.org/abs/2004.01316
Berg, T.L., Berg, A.C., Shih, J. (2010). Automatic Attribute Discovery and Characterization from Noisy Web Data. In Daniilidis, K., Maragos, P., Paragios, N. (eds) Computer Vision – ECCV 2010. Lecture Notes in Computer Science, vol 6311 (pp. 663-676). Springer, Berlin, Heidelberg. https://link.springer.com/chapter/10.1007/978-3-642-15549-9_48
Bossard, L., Dantone, M., Leistner, C., Wengert, C., Quack, T., & Gool, L. V. (2012, November). Apparel classification with style. In Asian conference on co mputer vision (pp. 321-335). Springer, Berlin, Heidelberg. https://link.springer.com/chapter/10.1007/978-3-642-37447-0_25
Chen, H., Gallagher, A., Girod, B. (2012). Describing Clothing by Semantic Attributes. In Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds) Computer Vision – ECCV 2012 (pp. 609-623). Lecture Notes in Computer Science, vol 7574. Springer, Berlin, Heidelberg. https://link.springer.com/chapter/10.1007/978-3-642-33712-3_44
Choutas, V., Müller, L., Huang, C. H. P., Tang, S., Tzionas, D., & Black, M. J. (2022). Accurate 3D Body Shape Regression Using Metric and Semantic Attributes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2718-2728). Computer Vision Foundation. https://openaccess.thecvf.com/content/CVPR2022/html/Choutas_Accurate_3D_Body_Shape_Regression_Using_Metric_and_Semantic_Attributes_CVPR_2022_paper.html
Csanák, E. (2014). Eco-friendly concepts and ethical movements in the fashion industry. In International Textile, Clothing and Design Conference—Magic World of Textiles (pp.1-7). Dubrownik, Croatia. https://www.researchgate.net/publication/283211577_ECO-FRIENDLY_CONCEPTS_AND_ETHICAL_MOVEMENTS_IN_THE_FASHION_INDUSTRY
Farhadi, A., Hejrati, M., Sadeghi, M. A., Young, P., Rashtchian, C., Hockenmaier, J., & Forsyth, D. (2010). Every picture tells a story: Generating sentences from images. In European conference on computer vision (pp. 15-29). Springer, Berlin, Heidelberg. https://link.springer.com/chapter/10.1007/978-3-642-15561-1_2
Hadi Kiapour, M., Han, X., Lazebnik, S., Berg, A. C., & Berg, T. L. (2015). Where to buy it: Matching street clothing photos in online shops. In Proceedings of the IEEE international conference on computer vision (pp. 3343-3351). Computer Vision Foundation. https://www.cv-foundation.org/openaccess/content_iccv_2015/html/Kiapour_Where_to_Buy_ICCV_2015_paper.html
Huang, J., Feris, R. S., Chen, Q., & Yan, S. (2015). Cross-domain image retrieval with a dual attribute-aware ranking network. In Proceedings of the IEEE international conference on computer vision (pp. 1062-1070). Computer Vision Foundation. https://www.cv-foundation.org/openaccess/content_iccv_2015/html/Huang_Cross-Domain_Image_Retrieval_ICCV_2015_paper.html
Indonesia Belajar. (2021, June 1). SKLearn 16 Random Forest Classification Hutan Acak Belajar Machine Learning Dasar [Video]. Youtube: https://www.youtube.com/watch?v=yKovaQ6tyV8
Jia, M., Zhou, Y., Shi, M., & Hariharan, B. (2018). A deep-learning-based fashion attributes detection model. arXiv preprint arXiv:1810.10148. https://arxiv.org/abs/1810.10148
Kiapour, M. H., & Piramuthu, R. (2018). Brand> logo: Visual analysis of fashion brands. arXiv preprint arXiv:1810.09941. https://openaccess.thecvf.com/content_ECCVW_2018/papers/11131/Kiapour_Brand__Logo_Visual_Analysis_of_Fashion_Brands_ECCVW_2018_paper.pdf
Kavlakoglu, E. (2020, May 27). Learning vs. Deep Learning vs. Neural Networks: What’s the Difference?. ibm.com. https://www.ibm.com/cloud/blog/ai-vs-machine-learning-vs-deep-learning-vs-neural-networks
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). https://openaccess.thecvf.com/content_cvpr_2016/html/Liu_DeepFashion_Powering_Robust_CVPR_2016_paper.html
Nasteski, V. (2017). An overview of the supervised machine learning methods. Horizons. b, 4, pp.51-62. https://www.researchgate.net/publication/328146111_An_overview_of_the_supervised_machine_learning_methods
Sarkar, K., Mehta, D., Xu, W., Golyanik, V., & Theobalt, C. (2020, August). Neural re-rendering of humans from a single image. In European Conference on Computer Vision (pp. 596-613). Springer, Cham. https://link.springer.com/chapter/10.1007/978-3-030-58621-8_35
Wired. (2021, August 18). Computer Scientist Explains Machine Learning in 5 Levels of Difficulty [Video]. YouTube. https://www.youtube.com/watch?v=5q87K1WaoFI
Yu, C., Hu, Y., Chen, Y., & Zeng, B. (2019). Personalized fashion design. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 9046-9055). https://openaccess.thecvf.com/content_ICCV_2019/html/Yu_Personalized_Fashion_Design_ICCV_2019_paper.html
Zhao, B., Feng, J., Wu, X., & Yan, S. (2017). Memory-augmented attribute manipulation networks for interactive fashion search. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1520-1528). https://openaccess.thecvf.com/content_cvpr_2017/html/Zhao_Memory-Augmented_Attribute_Manipulation_CVPR_2017_paper.html
Zou, X., Wong, W. K., & Mo, D. (2018, June). Fashion meets AI technology. In International Conference on Artificial Intelligence on Textile and Apparel (pp. 255-267). Springer, Cham. https://link.springer.com/chapter/10.1007/978-3-319-99695-0_31
Authors
This work is licensed under a Creative Commons Attribution 4.0 International License.
- The Author shall grant to the Publisher and its agents the nonexclusive perpetual right and license to publish, archive, and make accessible the Work in whole or in part in all forms of media now or hereafter known under a Creative Commons Attribution 4.0 License or its equivalent, which, for the avoidance of doubt, allows others to copy, distribute, and transmit the Work under the following conditions:
- Attribution: other users must attribute the Work in the manner specified by the author as indicated on the journal Web site;
With the understanding that the above condition can be waived with permission from the Author and that where the Work or any of its elements is in the public domain under applicable law, that status is in no way affected by the license.
- The Author is able to enter into separate, additional contractual arrangements for the nonexclusive distribution of the journal's published version of the Work (e.g., post it to an institutional repository or publish it in a book), as long as there is provided in the document an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post online a pre-publication manuscript (but not the Publisher's final formatted PDF version of the Work) in institutional repositories or on their Websites prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (see The Effect of Open Access). Any such posting made before acceptance and publication of the Work shall be updated upon publication to include a reference to the Publisher-assigned DOI (Digital Object Identifier) and a link to the online abstract for the final published Work in the Journal.
- Upon Publisher's request, the Author agrees to furnish promptly to Publisher, at the Author's own expense, written evidence of the permissions, licenses, and consents for use of third-party material included within the Work, except as determined by Publisher to be covered by the principles of Fair Use.
- The Author represents and warrants that:
- The Work is the Author's original work;
- The Author has not transferred, and will not transfer, exclusive rights in the Work to any third party;
- The Work is not pending review or under consideration by another publisher;
- The Work has not previously been published;
- The Work contains no misrepresentation or infringement of the Work or property of other authors or third parties; and
- The Work contains no libel, invasion of privacy, or other unlawful matter.
- The Author agrees to indemnify and hold Publisher harmless from Author's breach of the representations and warranties contained in Paragraph 7 above, as well as any claim or proceeding relating to Publisher's use and publication of any content contained in the Work, including third-party content.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Article Details
Accepted 2023-01-30
Published 2023-01-30