O
Orclever
Back to Journal
Research Article Open AccessOrclever Native

Embedding of Regional Adjacency Graph in Textile Image Classification with Deep Learning Application

Ömer Akgüller1✉,
Mehmet Ali Balcı2,
Aysu İldeniz3,
Duygu Yavuzkasap Ayakta4
1Mugla Sitki Kocman University
2Mugla Sitki Kocman University
3YUNSA, Research and Developement Department
4YUNSA, Research and Developement Department
Received:Apr 23, 2022→Accepted:May 31, 2022→Published:June 7, 2022
DOI: 10.56038/ejrnd.v2i2.71
Vol. 2, No. 2
3 Views0 Downloads0 Cited by
AI SummaryAI-generated · verify against the full text

Researchers used graph embeddings to improve image classification in machine learning, specifically in textile images.

The study proposed a method using machine learning-based embeddings of weighted regional graphs for image classification. This approach was applied to textile image classification, a common problem in machine learning. The method used graph embeddings to improve classification accuracy, but specific results and findings are not mentioned in the provided text.

Abstract

The image classification problem is a process that many machine learning methods are trying to solve. Graphs, which are combinatorial mathematical structures, are frequently used in machine learning problems. In this study, a method using machine learning based embeddings of weighted regional graphs for image classification problem is proposed.

Keywords
Laplacian MatrixRegional GraphImage ClassificationTextile Engineering

References

  1. 1.Babina, T., Fedyk, A., He, A. X., & Hodson, J. (2020). Artificial intelligence, firm growth, and industry concentration. Firm Growth, and Industry Concentration (November 22, 2020).
  2. 2.Bolton, C., Machová, V., Kovacova, M., & Valaskova, K. (2018). The power of human–machine collaboration: Artificial intelligence, business automation, and the smart economy. Economics, Management, and Financial Markets, 13(4), 51-56.
  3. 3.Thoben, K. D., Wiesner, S., & Wuest, T. (2017). “Industrie 4.0” and smart manufacturing-a review of research issues and application examples. International journal of automation technology, 11(1), 4-16.
  4. 4.Bullon, J., González Arrieta, A., Hernández Encinas, A., & Queiruga Dios, A. (2017). Manufacturing processes in the textile industry. Expert Systems for fabrics production.
  5. 5.Perez, J. J. B., Arrieta, A. G., Encinas, A. H., & Dios, M. A. Q. (2017). Manufacturing processes in the textile industry. Expert Systems for fabrics production. Adcaij-Advances in Distributed Computing and Artificial Intelligence Journal, 6(4), 15-23.
  6. 6.Hanbay, K., Talu, M. F., & Özgüven, Ö. F. (2016). Fabric defect detection systems and methods—A systematic literature review. Optik, 127(24), 11960-11973.
  7. 7.Mahajan, P. M., Kolhe, S. R., & Patil, P. M. (2009). A review of automatic fabric defect detection techniques. Advances in Computational Research, 1(2), 18-29.
  8. 8.Song, L., Li, R., & Chen, S. (2020). Fabric defect detection based on membership degree of regions. IEEE Access, 8, 48752-48760.
  9. 9.Perozzi, B., Al-Rfou, R., & Skiena, S. (2014, August). Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 701-710).
  10. 10.Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems, 26.
  11. 11.Grover, A., & Leskovec, J. (2016, August). node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 855-864).
  12. 12.Cao, S., Lu, W., & Xu, Q. (2015, October). Grarep: Learning graph representations with global structural information. In Proceedings of the 24th ACM international on conference on information and knowledge management (pp. 891-900).
  13. 13.Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., & Mei, Q. (2015, May). Line: Large-scale information network embedding. In Proceedings of the 24th international conference on world wide web (pp. 1067-1077).
  14. 14.Lauzon, F. Q. (2012, July). An introduction to deep learning. In 2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA) (pp. 1438-1439). IEEE.
  15. 15.Chan, T. H., Jia, K., Gao, S., Lu, J., Zeng, Z., & Ma, Y. (2015). PCANet: A simple deep learning baseline for image classification?. IEEE transactions on image processing, 24(12), 5017-5032.
  16. 16.Sadad, T., Khan, A. R., Hussain, A., Tariq, U., Fati, S. M., Bahaj, S. A., & Munir, A. (2021). Internet of medical things embedding deep learning with data augmentation for mammogram density classification. Microscopy Research and Technique, 84(9), 2186-2194.
  17. 17.Minaee, S., Kalchbrenner, N., Cambria, E., Nikzad, N., Chenaghlu, M., & Gao, J. (2021). Deep learning--based text classification: a comprehensive review. ACM Computing Surveys (CSUR), 54(3), 1-40.References should be cited in the text by name and year in parentheses.
  18. 18.Example:
  19. 19.Khare, S. K., & Bajaj, V. (2020). Time–frequency representation and convolutional neural network-based emotion recognition. IEEE transactions on neural networks and learning systems, 32(7), 2901-2909.
  20. 20.Oralhan, Z., Oralhan, B., & Yiğit, Y. (2017). Smart city application: Internet of things (IoT) technologies based smart waste collection using data mining approach and ant colony optimization. Internet Things, 14(4), 5.
Cite This Article
Akgüller, Ö., Balcı, M. A., İldeniz, A., Ayakta, D. Y. (2022). Embedding of Regional Adjacency Graph in Textile Image Classification with Deep Learning Application. *The European Journal of Research and Development*, 2(2), 315 - 328. https://doi.org/10.56038/ejrnd.v2i2.71

Bibliographic Info

JournalThe European Journal of Research and Development
Volume2
Issue2
Pages315–328
PublishedJune 7, 2022
eISSN2822-2296

Indexing & License

Open AccessCC BY 4.0CrossRef DOIORCIDOAI-PMH

More from The European Journal of Research and Development

Challenges in Maize Root Phenotyping: Preprocessing Limits and Class Imbalance in Deep Learning

Hüdanur Engin, Ali Murat Tiryaki

2026 · Vol 6 · Issue 1

The Bleaching of Woven Fabrics Using the Foam Application Technique

Aylin Kuşen, Onur Balcı, Koray Pektaş

2026 · Vol 6 · Issue 1

Automated Monkeypox Disease Classification Using Texture and Focus-Based Image Features

Tuğba Şentürk, Çiğdem Gülüzar Altıntop, Fatma Latifoğlu

2026 · Vol 6 · Issue 1

EEG-Based Assessment of Stress Levels Using Time–Frequency Features and Machine Learning

Sevde Samsa, Çiğdem Gülüzar Altıntop

2026 · Vol 6 · Issue 1

A Compact Non-Intrusive Measurement System for Critical Dimensions and Calibration Chart Generation of Underground Fuel Tanks

İlker Değirmencioğlu, Savaş Barış, Yusuf Kaya

2025 · Vol 5 · Issue 1

An AI-Based Question–Answering System for Corporate Documents: VK ArtiFin

Zeynep Örpek, Büşra Tural, Zeynep Destan

2025 · Vol 5 · Issue 1

Browse all articles in The European Journal of Research and Development →