Skip to main content
SpringerLink
Log in

Framework of CNN Architecture for Fashion Image Classification

  • Conference paper
  • First Online:
Applied Computational Technologies (ICCET 2022)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 303))

Included in the following conference series:

Abstract

Nowadays, online shopping is booming due to the pandemic. As stores are not operational during the lockdown, retailers try to sell everything online. Also, competition is increasing between them, allowing retailers to enhance their online application features. The fashion business has recently added many features like clothes search, clothes recommendation, etc. All these features can be implemented with the help of image classification. Clothes have many attributes and hidden features which can be extracted and classified with the help of Deep learning. This paper implements a CNN-based architecture, a deep learning approach, to classify images. This architecture is trained on the Fashion MNIST dataset. Proposed CNN architecture is examined, and substantial results are obtained.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 299.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 299.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Hu, W., Huang, Y., Wei, L., Zhang, F., Li, H.: Deep convolutional neural networks for hyper spectral image classification. J. Sens. (2015)

    Google Scholar 

  2. Albawi, S., Mohammed, T.A.: Understanding of a convolutional neural network. In: ICET2017, Antalya, Turkey. IEEE (2017)

    Google Scholar 

  3. Kadam, S.S., Adamuthe, A.C., Patil, A.B.: CNN model for image classification on MNIST and fashion-MNIST dataset. J. Sci. Res. 64(2), 11 (2020)

    Google Scholar 

  4. Zhang, J., Zhan, Z., Cheng, K., Zhan, Y.: Review on development of deep learning. J. Jiangsu Univ. Nat. Sci. Ed. 36(2), 191–200 (2015)

    Google Scholar 

  5. Zhou, W., Li, H., Tian, Q.: Recent advance in content-based image retrieval: a literature survey (2017)

    Google Scholar 

  6. Saritha, R.R., Paul, V., Kumar, P.G.: Content based image retrieval using deep learning process. Clust. Comput. 22(2), 4187–4200 (2018). https://doi.org/10.1007/s10586-018-1731-0

    Article  Google Scholar 

  7. Le Cun, B.B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D.: Handwritten digit recognition with a back-propagation network. In: Advances in Neural Information Processing System, pp. 396–404. Morgan Kaufmann Publishers Inc. (1990)

    Google Scholar 

  8. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)

    Article  Google Scholar 

  9. Qayyum, A., Anwar, S.M., Awais, M., Majid, M.: Medical image retrieval using deep convolutional neural network. Neurocomputing 266, 8–20 (2017)

    Article  Google Scholar 

  10. Li, Q., Cai, W., Wang, X., Zhou, Y., Feng, D.D., Chen, M.: Medical image classification with convolutional neural network. In: 13th International Conference on Control, Automation, Robotics & Vision Marina Bay Sands, Singapore, 10–12th December 2014 (ICARCV 2014). IEEE (2014)

    Google Scholar 

  11. Bhatnagar, S., Ghosal, D., Kolekar, M.: Classification of fashion article images using convolutional neural networks. In: Fourth International Conference on Image Information Processing (ICIIP). IEEE (2017)

    Google Scholar 

  12. Alzubaidi, L., et al.: Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J. Big Data 8(1), 1–74 (2021). https://doi.org/10.1186/s40537-021-00444-8

    Article  Google Scholar 

  13. O’Shea, K., Nash, R.: An introduction to convolutional neural networks. ArXiv e-prints (2015)

    Google Scholar 

  14. https://www.kaggle.com/zalando-research/fashionmnist

  15. Ying, X.: An overview of overfitting and its solutions. In: Journal of Physics: Conference Series, vol. 1168, p. 022022 (2019). https://doi.org/10.1088/1742-6596/1168/2/022022

  16. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014)

    MathSciNet  MATH  Google Scholar 

  17. Vakili, M., Ghamsari, M., Rezaei, M.: Performance analysis and comparison of machine and deep learning algorithms for IoT data classification. arXiv:2001.09636 (2020)

  18. Xiao, H., Rasul, K., Vollgraf, R.: Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747 (2017)

  19. Sivasankaran, D., Sai Seena, P., Rajesh, R., Kanmand, M.: Sketch based image retrieval using deep learning based machine learning. Int. J. Eng. Adv. Technol. (IJEAT) 10(5) (2021). ISSN: 2249-8958

    Google Scholar 

  20. Dufourq, E., Bassett, B.A.: EDEN: evolutionary deep networks for efficient machine learning. arXiv preprint arXiv:1709.09161 (2017)

  21. Steffens, A., et al.: Classifying garments from fashion-MNIST dataset through CNNs. Adv. Sci. Technol. Eng. Syst. J. 6, 989–994 (2021). https://doi.org/10.25046/aj0601109

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Thadomal Shahani Engineering College, Mumbai, Maharashtra, India

    Juhi Janjua & Archana Patankar

Authors
  1. Juhi Janjua
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Archana Patankar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Juhi Janjua .

Editor information

Editors and Affiliations

  1. Department of Electronics and Telecommunication Engineering, Dr. B. A. Technological University, Lonere, Maharashtra, India

    Brijesh Iyer

  2. Department of Digital Education and Policy, Swansea University, Swansea, UK

    Tom Crick

  3. Creative Technologies and Product Design, National Taipei University of Business, Taipei City, Taiwan

    Sheng-Lung Peng

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Janjua, J., Patankar, A. (2022). Framework of CNN Architecture for Fashion Image Classification. In: Iyer, B., Crick, T., Peng, SL. (eds) Applied Computational Technologies. ICCET 2022. Smart Innovation, Systems and Technologies, vol 303. Springer, Singapore. https://doi.org/10.1007/978-981-19-2719-5_9

Download citation

Publish with us

Policies and ethics

Search

Navigation