Exploring databases for training models in machine learning in the Fashion industry

Authors

DOI:

https://doi.org/10.29147/datjournal.v9i2.877

Keywords:

Machine Learning in Fashion, Artificial intelligence, Technological innovation, Data base, Supervised learning

Abstract

Growing interest in applying machine learning (ML) to fashion highlights the importance of using labeled data to develop models, facilitating research replication, and automating the analysis of new data, such as fashion show images available online. Despite this need, few studies, especially in Brazil, methodologically explore the intersection between fashion and AM. This research aims to provide an overview of online databases for training ML models. A systematic review identified 26 articles that use these databases, such as Fashion-MNIST and DeepFashion2. Content analysis revealed that these databases, including Polyvore and Fashion Image Dataset, have diverse applications, highlighting the transformative potential of AM in fashion and encouraging innovations in design, production, and marketing in the fashion industry.

Downloads

Download data is not yet available.

Author Biographies

Ítalo José de Medeiros Dantas

Doutorando em Processos e Manifestações Culturais, onde é bolsistas PROSUC/ CAPES. Posssui Mestrado em Design (UFCG) e Graduação em Design de Moda (IFRN).

Marcelo Curth

Possui doutorado em Administração pela Universidade do Vale do Rio dos Sinos (UNISINOS), mestrado em Administração e Negócios pela Universidade Católica do Rio Grande do Sul (PUC-RS), Pós-Graduado em Administração e Marketing pela Universidade Gama Filho, Pós-Graduado em Educação pela Faculdade (SENAC-RS) e pós-graduando em Mentoring Teacher Education (Universidade de Tampere - Finlândia) e graduação em Ciências do Desporto pela Universidade Luterana do Brasil (ULBRA). É professor do PPG em Processos e Manifestações Culturais da Universidade Feevale, atuando como pesquisador no tema Marketing: Identidade e Cultura.

Aline Gabriel Freire

Mestre em Engenharia Têxtil pela Universidade Federal do Rio Grande do Norte. Professora de Moda e Vestuário no Instituto Federal de Educação, Ciência e Tecnologia do Rio Grande do Norte.

References

AGGARWAL, P. Fashion product images dataset. 2024. Disponível em: https://www.kaggle.com/datasets/paramaggarwal/fashion-product-images-dataset. Acesso em: 18 jan. 2024.

ALLOGHANI, M. et al. A Systematic Review on Supervised and Unsupervised Machine Learning Algorithms for Data Science. In: BERRY, M.; MOHAMED, A.; YAP, B. (Eds.) Supervised and Unsupervised Learning for Data Science. Unsupervised and Semi-Supervised Learning. Springer, Cham, 2020. DOI: https://doi.org/10.1007/978-3-030-22475-2_1

ALZU’BI, A. et al. An interactive attribute-preserving fashion recommendation with 3D image-based virtual try-on. International Journal of Multimedia Information Retrieval, v. 12, n. 2, p. 24, 2023. DOI: https://doi.org/10.1007/s13735-023-00294-5

ARKSEY, H.; O’MALLEY, L. Scoping studies: towards a methodological framework. International Journal of Social Research Methodology, v. 8, n. 1, p. 19–32, 2005. DOI: https://doi.org/10.1080/1364557032000119616

BERTOLINI, M.; MEZZOGORI, D.; NERONI, M.; ZAMMORI, F. Machine Learning for industrial applications: A comprehensive literature review. Expert Systems with Applications, v. 175, p. 114820, 2021. DOI: https://doi.org/10.1016/j.eswa.2021.114820

BHOIR, S.; PATIL, S. The FASHION Visual Search using Deep Learning Approach. Library Philosophy and Practice, n. 7569, n. p., 2023. DOI: https://doi.org/10.21203/rs.3.rs-2053297/v1

CASTRO, E. et al. Fill in the blank for fashion complementary outfit product retrieval: Visum summer school competition. Machine Vision and Applications, v. 34, n. 1, p. 16, 2023. DOI: https://doi.org/10.1007/s00138-022-01359-x

CENDON, B. V.; RIBEIRO, N. A. Análise da literatura acadêmica sobre o portal periódico capes. Informação & Sociedade, v. 18, n. 2, 2008.

CHAKRABORTY, S. et al. Fashion recommendation systems, models, and methods: A review. Informatics, v. 8, p. 49, 2021. DOI: https://doi.org/10.3390/informatics8030049

CHANG, A. A. et al. Fashion trend forecasting using machine learning techniques: a review. In: Data Science and Intelligent Systems: Proceedings of 5th Computational Methods in Systems and Software 2021, Vol. 2, p. 34–44, 2021. DOI: https://doi.org/10.1007/978-3-030-90321-3_5

CHANG, Y.-H.; ZHANG, Y.-Y. Deep learning for clothing style recognition using yolov5. Micromachines, v. 13, n. 10, p. 1678, 2022. DOI: https://doi.org/10.3390/mi13101678

CUI, X. An adaptive recommendation algorithm of intelligent clothing design element based on large database. Mobile Information Systems, v. 2022, p. 3334047, 2022. DOI: https://doi.org/10.1155/2022/3334047

FONTANINI, T.; FERRARI, C. Would Your Clothes Look Good on Me? Towards Transferring Clothing Styles with Adaptive Instance Normalization. Sensors, v. 22, n. 13, p. 5002, 2022. DOI: https://doi.org/10.3390/s22135002

GE, Y.; ZHANG, R.; WU, L.; WANG, X.; TANG, X.; LUO, P. A Versatile Benchmark for Detection, Pose Estimation, Segmentation and Re-Identification of Clothing Images. In: CVPR, 2019. DOI: https://doi.org/10.1109/CVPR.2019.00548

GETMAN, R. R. et al. Machine learning (ML) for tracking fashion trends: Documenting the frequency of the baseball cap on social media and the runway. Clothing and Textiles Research Journal, v. 39, n. 4, p. 281–296, 2021. DOI: https://doi.org/10.1177/0887302X20931195

GIRI, C. et al. A detailed review of artificial intelligence applied in the fashion and apparel industry. IEEE Access, v. 7, p. 95376–95396, 2019. DOI: https://doi.org/10.1109/ACCESS.2019.2928979

GU, X. et al. Fashion analysis and understanding with artificial intelligence. Information Processing & Management, v. 57, n. 5, p. 102276, 2020. DOI: https://doi.org/10.1016/j.ipm.2020.102276

GUAN, C.; QIN, S.; LONG, Y. Apparel-based deep learning system design for apparel style recommendation. International Journal of Clothing Science and Technology, v. 31, n. 3, p. 376-389, 2019. DOI: https://doi.org/10.1108/IJCST-02-2018-0019

GUO, S.; HUANG, W.; ZHANG, X.; SRIKHANTA, P.; CUI, Y.; LI, Y.; BELONGIE, S. The Imaterialist Fashion Attribute Dataset. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, 2019. DOI: https://doi.org/10.1109/ICCVW.2019.00377

HAENLEIN, M.; KAPLAN, A. A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California Management Review, v. 61, n. 4, p.5–14, 2019. DOI: https://doi.org/10.1177/0008125619864925

HAN, A.; KIM, J.; AHN, J. Color trend analysis using machine learning with fashion collection images. Clothing and Textiles Research Journal, v. 40, n. 4, p. 308–324, 2022. DOI: https://doi.org/10.1177/0887302X21995948

HAN, X.; WU, Z.; JIANG, Y.-G.; DAVIS, L. S. Learning Fashion Compatibility with Bidirectional LSTMs. In: ACM Multimedia, 2017. DOI: https://doi.org/10.1145/3123266.3123394

HUYEN, C. Designing Machine Learning Systems. O’Reilly Media, Inc., 2022.

KIM, S. et al. Automatic Measurements of Garment Sizes Using Computer Vision Deep Learning Models and Point Cloud Data. Applied Sciences, v. 12, n. 10, p. 5286, 2022. DOI: https://doi.org/10.3390/app12105286

LEE, Y. A. Trends of Emerging Technologies in the Fashion Product Design and Development Process. In: LEE, Y. A. (Ed.). Leading Edge Technologies in Fashion Innovation. Palgrave Studies in Practice: Global Fashion Brand Management. Cham: Palgrave Mac-millan, 2022. p. 1-15. DOI: https://doi.org/10.1007/978-3-030-91135-5_1

LIPOVETSKY, G. O império do efêmero: a moda e seu destino nas sociedades modernas. São Paulo: Editora Companhia das Letras, 2009.

LIU, J.; SONG, X.; CHEN, Z.; MA, J. Neural fashion experts: I know how to make the complementary clothing matching. Neurocomputing, v. 359, p. 249-263, 2019. DOI: https://doi.org/10.1016/j.neucom.2019.05.081

LU, Y. Artificial intelligence: a survey on evolution, models, applications, and future trends. Journal of Management Analytics, v. 6, n. 1, p. 1-29, 2019. DOI: https://doi.org/10.1080/23270012.2019.1570365

LUCE, L. Artificial intelligence for fashion: How AI is revolutionizing the fashion industry. Apress, 2018. DOI: https://doi.org/10.1007/978-1-4842-3931-5

MAHESH, B. Machine Learning Algorithms - A Review. International Journal of Science and Research, v. 9, n. 1, p. 381-386, 2020. DOI: https://doi.org/10.21275/ART20203995

MARTÍN-MARTÍN, A. et al. Google scholar, microsoft academic, scopus, dimensions, web of science, and opencitations coci: a multidisciplinary comparison of coverage via citations. Scientometrics, v. 126, n. 1, p. 871-906, 2021. DOI: https://doi.org/10.1007/s11192-020-03690-4

MATZEN, K.; BALA, K.; SNAVELY, N. Streetstyle: Exploring world-wide clothing styles from millions of photos. arXiv preprint arXiv:1706.01869, 2017.

MIRANDA, A. P. Consumo de moda: a relação pessoa-objeto. São Paulo: Editora estação das letras e cores, 2019.

MUTHUKRISHNAN, N. et al. Brief history of artificial intelligence. Neuroimaging Clinics, v. 30, n. 4, p. 393-399, 2020. DOI: https://doi.org/10.1016/j.nic.2020.07.004

PACKER, A. L. The scielo open access: a gold way from the south. Canadian Journal of Higher Education, v. 39, n. 3, p. 111-126, 2009. DOI: https://doi.org/10.47678/cjhe.v39i3.479

PARÉ, G. et al. Synthesizing information systems knowledge: A typology of literature reviews. Information & Management, v. 52, n. 2, p. 183-199, 2015. DOI: https://doi.org/10.1016/j.im.2014.08.008

PENG, D. et al. Unsupervised multi-modal modeling of fashion styles with visual attributes. Applied Soft Computing, v. 115, 108214, 2022. DOI: https://doi.org/10.1016/j.asoc.2021.108214

RATHORE, B. Beyond trends: Shaping the future of fashion marketing with ai, sustainability and machine learning. Eduzone, v. 6, n. 2, p. 16-24, 2017. DOI: https://doi.org/10.56614/eiprmj.v6i2y17.340

ROCHA, D.; SOARES, F.; OLIVEIRA, E.; CARVALHO, V. Blind people: Clothing category classification and stain detection using transfer learning. Applied Sciences, v. 13, n. 3, p. 1925, 2023. DOI: https://doi.org/10.3390/app13031925

SHIN, S.-Y.; JO, G.; WANG, G. A novel method for fashion clothing image classification based on deep learning. Journal of Information and Communication Technology, v. 22, n. 1, p. 127–148, 2023. DOI: https://doi.org/10.32890/jict2023.22.1.6

SHINDE, P. P.; SHAH, S. A review of machine learning and deep learning applications. In:

Fourth international conference on computing communication control and automation (ICCUBEA), p. 1–6. IEEE, 2018. DOI: https://doi.org/10.1109/ICCUBEA.2018.8697477

SIPPER, M. Combining Deep Learning with Good Old-Fashioned Machine Learning. SN COMPUT. SCI., v. 4, p. 85, 2023. DOI: https://doi.org/10.1007/s42979-022-01505-2

STUART, R.; NORVIG, P. Artificial Intelligence: A Modern Approach. Upper Saddle River, NJ, USA: Prentice-Hall, 2009.

SULTHANA, R. et al. A review on the literature of fashion recommender system using deep learning. International Journal of Performability Engineering, v. 17, n. 8, p. 695, 2021. DOI: https://doi.org/10.23940/ijpe.21.08.p5.695702

SUN, L.; ZHAO, L. Technology disruptions: exploring the changing roles of designers, makers, and users in the fashion industry. International Journal of Fashion Design, Technology and Education, v. 11, n. 3, p. 362-374, 2018. DOI: https://doi.org/10.1080/17543266.2018.1448462

TAHERDOOST, H.; MADANCHIAN, M. Artificial intelligence and knowledge management: Impacts, benefits, and implementation. Computers, v. 12, n. 4, p. 72, 2023. DOI: https://doi.org/10.3390/computers12040072

TAUTKUTE, I. et al. Deepstyle: Multimodal search engine for fashion and interior design. IEEE Access, v. 7, p. 84613–84628, 2019. DOI: https://doi.org/10.1109/ACCESS.2019.2923552

VIJAYARAJ, A. et al. Deep learning image classification for fashion design. Wireless Communications and Mobile Computing, 2022. DOI: https://doi.org/10.1155/2022/7549397

XIAO, H.; RASUL, K.; VOLLGRAF, R. Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms. 2017. Disponível em: https://arxiv.org/abs/cs.LG/1708.07747. Acesso em: 18 jan. 2024.

YANG, B. Clothing design style recommendation using decision tree algorithm combined with deep learning. Computational Intelligence and Neuroscience, 2022. DOI: https://doi.org/10.1155/2022/5745457

YIAN, S.; KYUNG-SHIK, S. Hierarchical convolutional neural networks for fashion image classification. Expert Systems with Applications, v. 116, p. 328-339, 2019. DOI: https://doi.org/10.1016/j.eswa.2018.09.022

whaZHANG, S. et al. Watch fashion shows to tell clothing attributes. Neurocomputing, v. 282, p. 98–110, 2018. DOI: https://doi.org/10.1016/j.neucom.2017.12.027

Published

2024-09-09

How to Cite

Dantas, Ítalo J. de M., Curth, M., & Freire, A. G. (2024). Exploring databases for training models in machine learning in the Fashion industry. DAT Journal, 9(2), 157–174. https://doi.org/10.29147/datjournal.v9i2.877