Fashion Outfit Recommendation Based on Deep Learning Model

Authors

  • Bhagyshree Pravin Bhure  BE Student, Department of Computer Science and Engineering, Priyadrashini JL College of Engineering, Nagpur, Maharashtra, India
  • Pratiksha Tulshiram Bansod  BE Student, Department of Computer Science and Engineering, Priyadrashini JL College of Engineering, Nagpur, Maharashtra, India
  • Monali Shivram Amgaokar  BE Student, Department of Computer Science and Engineering, Priyadrashini JL College of Engineering, Nagpur, Maharashtra, India
  • Savita Pralhad Lodiwale  BE Student, Department of Computer Science and Engineering, Priyadrashini JL College of Engineering, Nagpur, Maharashtra, India
  • Anjali Pravin Orkey  BE Student, Department of Computer Science and Engineering, Priyadrashini JL College of Engineering, Nagpur, Maharashtra, India
  • Ashish Mohod  Assistant Professor, Department of Computer Science and Engineering, Priyadrashini JL College of Engineering, Nagpur, Maharashtra, India

Keywords:

Outfit recommendation, convolution neural network, embedding, clustering.

Abstract

Nowadays, the fashion industry is moving towards fast fashion, offering a large selection of garment products in a quicker and cheaper manner. Machine learning is completely changing the trends in the fashion industry. From big to small every brand is using machine learning techniques in order to improve their revenue, increase customers and stay ahead of the trend. People are into fashion and they want to know what looks best and how they can improve their style and elevate their personality. Traditional recommendations for clothes consisted of lexical methods. However, visual-based recommendations have gained popularity over the past few years. This involves processing a multitude of images using different image processing techniques. In order to handle such a vast quantity of images, deep neural networks have been used extensively. With the help of fast pre trained algorithms, these networks provide results which are extremely accurate, within a small amount of time. However, there are still ways in which recommendations for clothes can be improved. In this paper, we propose a deep learning based system which operates as a personal assistant to a fashion user. The system’s architecture and all its components are presented, with emphasis on the data collection and data clustering subsystems. In our use case scenario, datasets of garment products are retrieved from Kaggle website which contains different outfit which includes t-shirt, shirts, shoes, dress, etc nearly 20 + categories. With this dataset, we trained our CNN model and the performance of it is over 84%. And our model can also recommend daily outfit to users.

References

  1. F. Isinkaye, Y. Folajimi and B. Ojokoh, "Recommendation systems: principles, methods and evaluation", Egyptian Informatics J., vol. 16, no. 3, pp. 261-273, 2015. https://en.wikipedia.org/wiki/Artificial_neural_network, Accessed: 12-Apr-2018].
  2. R. Girshick et al, "Rich feature hierarchies for accurate object detection and semantic segmentation," in 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014. DOI: 10.1109/CVPR.2014.81.
  3. R. Girshick, "Fast R-CNN," in 2015 IEEE International Conference on Computer Vision (ICCV), 2015. DOI: 10.1109/ICCV.2015.169.
  4. S. Ren et al, "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks," IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, (6), pp. 1137- 1149, 2017. DOI: 10.1109/TPAMI.2016.2577031.
  5. X. Hu, W. Zhu, and Q. Li, “HCRS, A hybrid clothes recommender system based on user,” in Proceedings of the Ratings And Product Features, pp. 270–274, 2014.
  6. L. Yu-Chu, Y. Kawakita, E. Suzuki, and H. Ichikawa, “Personalized clothing-recommendation system based on a modified bayesian network,” in Proceedings of the IEEE/IPSJ 12th International Symposium on Applications and the Internet (SAINT '12), vol. 59, pp. 414–417, July 2012.
  7. C. Limaksornkul, D. N. Nakorn, O. Rakmanee, and W. Viriyasitavat, “Smart closet: Statistical-based apparel recommendation system,” in Proceedings of the 2014 3rd ICT International Senior Project Conference (ICT-ISPC '14), pp. 155–158, March 2014.
  8. S. Liu, T. V. Nguyen, J. Feng, M. Wang, and S. Yan, “Hi, magic closet, tell me what to wear !,” ACM Multimedia, vol. 43, pp. 1333-1334, 2012.
  9. T. Iwata, S. Watanabe, and H. Sawada, “Fashion coordinates recommender system using photographs from fashion magazines,” in Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI '11), pp. 2262–2267, Catalonia, Spain, July 2011.
  10. D. Sha, D. Wang, X. Zhou, S. Feng, Y. Zhang, and G. Yu, “An approach for clothing recommendation based on multiple image attributes,” Web-Age Information Management, 2016.
  11. L. C. Wang, X. Y. Zeng, L. Koehl, and Y. Chen, “Intelligent fashion recommender system: Fuzzy logic in personalized garment design,” IEEE Transactions on Human-Machine Systems, vol. 45, no. 1, pp. 95–109, 2015.
  12. V. Jagadeesh, R. Piramuthu, A. Bhardwaj, W. Di and N. Sundaresan, "Large scale visual recommendations from street fashion images", Proc. of the 20th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data mining - KDD '14, 2014.
  13. Y. Hu, X. Yi and L. Davis, "Collaborative Fashion Recommendation: A Functional Tensor Factorization Approach", Proc. of the 23rd ACM Int. Conf. on Multimedia - MM '15, 2015.
  14. X. Zhang, et al., "Trip outfits advisor: location-oriented clothing recommendation", IEEE Trans. on Multimedia, pp. 1-1, 2017.
  15. E. Simo-Serra, S. Fidler, F. Moreno-Noguer and R. Urtasun, "Neuroaesthetics in fashion: modeling the perception of fashionability", 2015 IEEE Conf. on Comput. Vision and Pattern Recognition (CVPR), 2015.
  16. E. Shen, H. Lieberman and F. Lam, "What am I gonna wear? scenario-oriented recommendation", Proc. of the 12th Int. Conf. on Intelligent user interfaces - IUI '07, 2007.
  17. L. Yu-Chu, Y. Kawakita, E. Suzuki and H. Ichikawa, "Personalized clothing recommendation system based on a modified bayesian network", 2012 IEEE/IPSJ 12th Int. Symposium on Applications and the Internet, 2012.
  18. S. G. Eshwar, G. G. Prabhu J, A. V. Rishikesh, A. N. Charan and V. Umadevi, "Apparel classification using convolutional neural networks", 2016 Int. Conf. on ICT in Business Industry & Government (ICTBIG), 2016
  19. A. Veit, B. Kovacs, S. Bell, J. McAuley, K. Bala and S. Belongie, "Learning visual clothing style with heterogeneous dyadic co-occurrences", 2015 IEEE Int. Conf. on Comput. Vision (ICCV), 2015.
  20. A. Krizhevsky, I. Sutskever and G. Hinton, "ImageNet classification with deep convolutional neural networks", Communications of the ACM, vol. 60, no. 6, pp. 84-90, 2017.
  21. S. Jiang, Y. Wu and Y. Fu, "Deep bi-directional cross-triplet embedding for cross-domain clothing retrieval", Proc. of the 2016 ACM on Multimedia Conf. - MM '16, 2016.
  22. Z. Liu, P. Luo, S. Qiu, X. Wang and X. Tang, "Deep Fashion: powering robust clothes recognition and retrieval with rich annotations", 2016 IEEE Conf. on Comput. Vision and Pattern Recognition (CVPR), 2016.
  23. K. Yamaguchi, M. Kiapour and T. Berg, "Paper doll parsing: retrieving similar styles to parse clothing items", 2013 IEEE Int. Conf. on Comput. Vision, 2013.

International Journal of Scientific Research in Science and Technology

Downloads

Published

2021-06-30

Issue

Volume 8, Issue 3, May-June-2021

Section

Research Articles

License

Copyright (c) IJSRST

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Bhagyshree Pravin Bhure, Pratiksha Tulshiram Bansod, Monali Shivram Amgaokar, Savita Pralhad Lodiwale, Anjali Pravin Orkey, Ashish Mohod "Fashion Outfit Recommendation Based on Deep Learning Model" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 8, Issue 3, pp.216-225, May-June-2021.