Abstract
There is a vested interest in applying Artificial Intelligence (AI) to fashion. However, studies have mainly focused on Western fashion. More inclusive automated fashion classification processes are required to categorize, organize, identify, advertise, recommend, and detect counterfeits for the global fashion industry. African fashion is a $31 billion industry, the second-largest economic sector of the continent after agriculture. This paper focused on a case study that relates to African culture, authenticity, living, and heritage. It presents a small Senegalese fashion dataset and a model capable of classifying various Senegalese apparels, called Boubous and Taille Mames by using transfer learning for image classification with MobileNetV2 as the base model. This paper raises the issue of the need of less western-centric datasets and proposes a preliminary reflection on addressing global AI-related fashion issues.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bossard, L., et al.: Apparel classification with style. In: Computer Vision – ACCV 2012, 2013, pp. 321–335
Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)
Deng, J., et al.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition (2009)
Donati, L., et al.: Fashion product classification through deep learning and computer vision. Appl. Sci. 9(7), 1385 (2019)
Dong, K., et al.: MOBILENETV2 model for image classification. In: 2020 2nd International Conference on Information Technology and Computer Application (ITCA) (2020)
Gebru, T., et al.: Datasheets for datasets. Commun. ACM 64(12), 86–92 (2021)
Hara, K., et al.: Fashion apparel detection: the role of deep convolutional neural network and pose-dependent priors. In: 2016 IEEE Winter Conference on Applications of Computer Vision (WACV) (2016)
Heilbron, F.C. et al.: Clothing recognition in the wild using the amazon catalog. In: 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) (2019), pp. 3145–3148 (2019)
Heng, S., et al.: Understanding AI ecosystems in the global south: the cases of senegal and Cambodia. Int. J. Inf. Manage. 64, 102454 (2022)
Henrique, A.S., et al.: Classifying garments from fashion-mnist dataset through Cnns. Adv. Sci., Technol. Eng. Syst. J. 6(1), 989–994 (2021)
Hong, C., et al.: Composite templates for cloth modeling and sketching. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (CVPR'06) (2006)
How to Prevent Discriminatory Outcomes in Machine Learning: World Economic Forum (2018). https://www3.weforum.org/docs/WEF_40065_White_Paper_How_to_Prevent _Discriminatory_Outcomes_in_Machine_Learning.pdf
Kastner, K.: Making fashion, forming bodies and persons in Urban Senegal. African J. Online, Africa Develop. (2019)
Kornblith, S., et al.: Do better imagenet models transfer better? In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
Shao, L., et al.: Transfer learning for visual categorization: a survey. IEEE Trans. Neural Netw. Learn. Syst. 26(5), 1019–1034 (2015)
Liu, Z., et al.: Fashion landmark detection in the wild. ArXiv.org, 10 Aug (2016)
Liu, Z., et al.: Deepfashion: powering robust clothes recognition and retrieval with rich annotations. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), N.p, pp. 1096–1104 (2016)
Maaten, L. V. D.: A new benchmark dataset for handwritten character recognition. Semant. Schol. (2009)
Mikolajczyk, À., Grochowski, M.: Data augmentation for improving deep learning in image classification problem. In: 2018 International Interdisciplinary PhD Workshop (IIPhDW) (2018)
Muhammed, M.A.E., et al.: Benchmark analysis of popular imagenet classification deep CNN architectures. In: 2017 International Conference On Smart Technologies For Smart Nation (SmartTechCon) (2017)
Raina, R., et al.: Self-taught learning. In: Proceedings of the 24th International Conference on Machine Learning - ICML ‘07 (2007)
Rohrmanstorfer, S., et al.: Image classification for the automatic feature extraction in human worn fashion data. Mathematics 9(6), 624 (2021)
Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. CoRR abs/1409.0575 (2015)
Sandler, M., et al.: MobileNetV2: inverted residuals and linear bottlenecks. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (2018)
Seo, Y., Shin, K.-S.: Image classification of fine-grained fashion image based on style using pre-trained convolutional neural network. In: 2018 IEEE 3rd International Conference on Big Data Analysis (ICBDA) (2018)
Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data 6(1) (2019)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. ArXiv.org (2015)
Tuinhof, H., et al.: Image-based fashion product recommendation with deep learning. In: International Conference on Machine Learning, Optimization, and Data Science, pp. 472–481 (2019)
Wang, W., et al.: Attentive fashion grammar network for fashion landmark detection and clothing category classification. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (2018)
Xiao, H., et al.: Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms. ArXiv.org (2017)
Acknowledgements
This work was partially funded by a Google TensorFlow grant.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Seck, A.C., Kaleemunnisa, Bathula, K.M., Scharff, C. (2023). Senegalese Fashion Apparels Classification System Using Deep Learning. In: Visvizi, A., Troisi, O., Grimaldi, M. (eds) Research and Innovation Forum 2022. RIIFORUM 2022. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-031-19560-0_60
Download citation
DOI: https://doi.org/10.1007/978-3-031-19560-0_60
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-19559-4
Online ISBN: 978-3-031-19560-0
eBook Packages: Computer ScienceComputer Science (R0)