Classification of Saudi Traditional Costumes using Deep Learning Technique | ||||
| International Design Journal | ||||
| Article 10, Volume 14, Issue 2 - Serial Number 60, March and April 2024, Page 129-140 PDF (1.28 MB) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/idj.2024.254693.1106 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Fardus Abdullah Baalamash  1; Rania Mostafa Kamal Debis2; Hassanin Al-Barhamtoshy1
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| 1King Abdulaziz University | ||||
| 2Professor of Clothing Manufacturing Technology, Department of Clothing and Textile, Faculty of Home Economics, King Abdulaziz University, Jeddah, | ||||
| Abstract | ||||
| This study aimed to develop a system for classifying traditional clothing from the Kingdom of Saudi Arabia using convolutional neural networks. A dataset of 339 images across 3 categories was collected and preprocessed, including normalization, cropping, and blurring to enhance image quality. The proposed system accomplished classification through a two-step process: Dataset collection utilizing Artificial intelligence (AI) techniques to gather 339 labeled images of clothing from Saudi Arabia which served as the training foundation, and classification using the Inception v3 CNN model with transfer learning. Testing is achieved with an overall accuracy of 84.85% based on a confusion matrix, demonstrating the model's ability to correctly classify traditional clothing. Weighted Euclidean distance matching was also implemented to retrieve the top 5 similarity matches for query images. A graphical user interface allows practical implementation and end-user clothing classification. The promising results indicate deep learning is viable for this application. However, limitations include the small dataset size. Future work involves collecting a larger, more diverse set of traditional Saudi clothing images to further improve classification performance. | ||||
| Keywords | ||||
| Saudi Traditional Clothing; Deep Learning; Inception v3 CNN model | ||||
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1- Aita, R. (2022). Big Data Analytics for Industry 4.0: A Literature Review. IEEE Access, 10, 35489-35503. https://doi.org/10.1109/ACCESS.2022.3086343
2- Al-Bassam, Fida. (2002): Hand Embroidery, Dar Al-Zahraa, first edition.
3- AlDabbagh, M. (2019). Traditional Clothing, Souvenirs, and Food as Factors of Tourist Attraction. Journal of Home Economics 29(1), 197-225. http://homeEcon.menofia.edu.eg 4- Al-Sahari, Aisha (2018): Designing a Virtual Museum of Traditional Costumes in the Kingdom of Saudi Arabia, International Design Magazine, 8(2), 87-101. http://search.mandumah.com/Record/985245 5- Aramco. (n. d.). Digitalization. Saudi Aramco. https://www.aramco.com/en/creating-value/technology-development/in-house-developed-technologies/digitalization
6- Bennett, Mike (2020): Blockchain, AI and knowledge graphs: What does it mean for the business. Journal of Securities Operations & Custody, 12 (4), 357-366. https://ideas.repec.org/a/aza/jsoc00/y2020v12i4p357-366.html
7- Choudhary, K., DeCost, B., Chen, C. et al. Recent advances and applications of deep learning methods in materials science. npj Comput Mater 8, 59 (2022). https://doi.org/10.1038/s41524-022-00734-6 8- Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications.
9- Fida, Laila (1993): Traditional clothing for women in Makkah Al-Mukarramah, its methods and embroidery, a field study, [Unpublished Master''''s thesis], College of Education for Home Economics
10- Haleem, A., Javaid, M., Qadri, M. A., Singh, R. P., & Suman, R. (2022). AI (AI) applications for marketing: A literature-based study. International Journal of Intelligent Networks, 3, 119–132. https://doi.org/10.1016/j.ijin.2022.08.005
11- Justinia, T. (2022). Saudi Arabia: Transforming Healthcare with Technology. In: Hübner, U.H., Mustata Wilson, G., Morawski, T.S., Ball, M.J. (eds) Nursing Informatics. Health Informatics. Springer, Cham. https://doi.org/10.1007/978-3-030-91237-6_47
12- Kamel, A. a. E. a. A., & El-Mougi, F. a. E. Z. (2020). A fuzzy decision support system for diagnosis of some liver diseases in educational medical institutions. International Journal of Fuzzy Logic and Intelligent System, 20(4), 358–368. https://doi.org/10.5391/ijfis.2020.20.4.358
13- Konagala, P. (2022): Big Data Analytics Using Apache Hive to Analyze Health Data. In Research Anthology on Big Data Analytics, Architectures, and Applications (pp. 979-992). IGI Global.
14- Korinek, A., Schindler, M., & Stiglitz, J. (2021). Technological Progress, AI, and Inclusive Growth, IMF Working Papers, 2021(166), A001. Retrieved Jul 1, 2023, from https://doi.org/10.5089/9781513583280.001.A001 15- Laplante, P. A. (2018). Dictionary of computer science. CRC Press.
16- Lattanzi, L., Raffaeli, R., Peruzzini, M., & Pellicciari, M. (2021). Digital twin for smart manufacturing: a review of concepts towards a practical industrial implementation. International Journal of Computer Integrated Manufacturing, 34(6), 567–597. https://doi.org/10.1080/0951192x.2021.1911003
17- Lindsay, G. W. (2021). Convolutional neural networks as a model of the visual system: past, present, and future. Journal of Cognitive Neuroscience, 33(10), 2017–2031. https://doi.org/10.1162/jocn_a_01544
18- Madulid, J. P. A., & Mayol, P. E. (2019). Clothing Classification using the Convolutional Neural Network Inception Model. Proceedings of the 2nd International Conference on Information Science and Systems. https://doi.org/10.1145/3322645.3322646
19- Mathworks. (n.d.). Comparing GUIDE and app designer. MATLAB & Simulink. https://www.mathworks.com/products/matlab/app-designer/comparing-guide-and-app-designer.html
20- Meena, G., Mohbey, K. K., & Kumar, S. (2023). Sentiment analysis on images using convolutional neural networks based Inception-V3 transfer learning approach. International Journal of Information Management Data Insights, 3(1), 100174. https://doi.org/10.1016/j.jjimei.2023.100174
21- Nirthika, R., Manivannan, S., Ramanan, A. et al. Pooling in convolutional neural networks for medical image analysis: a survey and an empirical study. Neural Comput & Applic 34, 5321–5347 (2022). https://doi.org/10.1007/s00521-022-06953-8 22- NIST. (2019). NIST AI Definition. https://www.nist.gov/itl/artificial-intelligence-portal/nist-ai-definition
23- Pantanella, F. (2022, July 18). How Fashion Designers find Inspiration. Beyond Talent Recruitment. https://beyondtalentrecruitment.com/blog/fashion-designers
24- Pasikowska-Schnass, M., Lim Y., (2023). AI in the context of cultural heritage and museums Complex challenges and new opportunities. https://www.europarl.europa.eu/RegData/etudes/BRIE/2023/747120/EPRS_BRI(2023)747120_EN.pdf
25- Razavian, A. S., Azizpour, H., Sullivan, J., & Carlsson, S. (2014). CNN Features Off-the-Shelf: An Astounding Baseline for Recognition. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, IEEE (pp. 512-519). https://doi.org/10.1109/CVPRW.2014.131
26- Saudi Arabia eGovernment Program. (n.d.). Ministry of Foreign Affairs. Retrieved June 30, 2023, from https://www.my.gov.sa/wps/portal/snp/main
27- Saudi Authority for Data and AI. (n.d.). Saudi Authority for Data and AI. https://sdaia.gov.sa/en/MediaCenter/News/Pages/NewsDetails.aspx?NewsID=168
28- Saudi Data and AI Authority. (n.d.). About us. Retrieved June 30, 2023, from https://www.sdaia.gov.sa/en/about-us/
29- Saudi Vision 2030. (n.d.). Digital transformation. Retrieved from https://www.vision2030.gov.sa/en/node/650
30- Saudigazette. (2019, June 29). Crown Prince’s speech at G20: Cooperation needed to face new challenges. https://saudigazette.com.sa/article/570379
31- Schindler, M., Korinek, A., & Stiglitz, J. E. (2021). Technological Progress, AI, and Inclusive Growth. IMF Working Paper, 2021 (166), 1. https://doi.org/10.5089/9781513583280.001
32- Sestino, A., Prete, M. G., Piper, L., & Guido, G. (2020). Internet of Things and Big Data as enablers for business digitalization strategies. Technovation, 98, 102173. https://doi.org/10.1016/j.technovation.2020.102173
33- Shah, R., Sridharan, N. V., Mahanta, T. K., Muniyappa, A., Vaithiyanathan, S., Ramteke, S. M., & Marian, M. (2023). Ensemble Deep Learning for Wear Particle Image Analysis. Lubricants, 11(11), 461. MDPI AG. Retrieved from https://dx.doi.org/10.3390/lubricants11110461
34- Sharma, A. (2021, July 4). Saudi Arabia unveils national strategy for data and AI. The National. https://www.thenationalnews.com/business/technology/saudi-arabia-unveils-national-strategy-for-data-and-ai-1.1097271
35- Shrouf, F., Ordieres, J., & Miragliotta, G. (2014). Smart factories in Industry 4.0: A review of the concept and of energy management approached in production based on the Internet of Things paradigm. https://doi.org/10.1109/ieem.2014.7058728
36- Su, H.; Wang, P.; Liu, L.; Li, H.; Li, Z.; Zhang, Y. Where to look and how to describe: Fashion image retrieval with an attentional heterogeneous bilinear network. IEEE Trans. Circuits Syst. Video Technol. 2020, 31, 3254–3265. 37- Taye, M.M. Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions. Computers 2023, 12, 91. https://doi.org/10.3390/computers12050091
38- Turkestani, Houria Bint Abdullah, and Makrish, Nora (2011): Designing embroidered pieces of art derived from traditional embroidery in the Makkah Al-Mukarramah region, Journal of Science and Arts, Studies and Research, Vol. 23, p. 4: pp. 127-145. http://search.mandumah.com/Record/110495
39- Vijayaraj, A., Vasanth Raj, P. T., Jebakumar, R., Gururama Senthilvel, P., Kumar, N., Suresh Kumar, R., & Dhanagopal, R. (2022). Deep Learning Image Classification for Fashion Design. Wireless Communications & Mobile Computing, 1–13. https://doi-org.sdl.idm.oclc.org/10.1155/2022/7549397 40- Wang, J., Yang, Y., Mao, J., Huang, Z., Huang, C., & Xu, W. (2016). Cnn-rnn: A unified framework for multi-label image classification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2285-2294. https://arxiv.org/ftp/arxiv/papers/1604/1604.04573.pdf
41- Ye, Q. (2023), "Metadata construction scheme of a traditional clothing digital collection", The Electronic Library, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/EL-01-2023-0004
42- Zawya. (2022, November 4). Saudi’s STC partners with Ericsson for AI-based solutions to enhance networks. ACE Times. https://www.zawya.com/en/business/technology-and-telecom/saudis-stc-partners-with-ericsson-for-ai-based-solutions-to-enhance-networks-ibc620cq
Zhou Z, Deng W, Wang Y, Zhu Z. Classification of clothing images based on a parallel convolutional neural network and random vector functional link optimized by the grasshopper optimization algorithm. Textile Research Journal. 2022;92(9-10):1415-1428. https://doi.org/10.1177/00405175211059207 | ||||
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