Depiction-inspired Recipe Generator Using Deep Learning

Year : 2024 | Volume :11 | Issue : 02 | Page : 47-55
By

Om Sanjay Awari,

Shivaraj Mahendra Ghangale,

Janmejay Aklesh Maurya,

Aishwarya Ajit Ghangale1,

Keerti Kharatmol,

  1. Student, Department of Computer Science and Engineering, K.C. College of Engineering & Management Studies & Research, Thane, Maharashtra, India
  2. Student, Department of Computer Science and Engineering, K.C. College of Engineering & Management Studies & Research, Thane, Maharashtra, India
  3. Student, Department of Computer Science and Engineering, K.C. College of Engineering & Management Studies & Research, Thane, Maharashtra, India
  4. Student, Department of Computer Science and Engineering, K.C. College of Engineering & Management Studies & Research, Thane, Maharashtra, India
  5. Assistant Professor, Department of Computer Science and Engineering, K.C. College of Engineering & Management Studies & Research, Thane, Maharashtra, India

Abstract

Machine learning has become a crucial part of modern life, influencing various domains. Its applications range from enhancing data-driven business decisions to enabling autonomous vehicles. Advances in machine learning have brought about notable changes in how we interact with technology. In the culinary world, the idea of creating food recipes from images has gained increasing interest. This entails the development of innovative systems that seamlessly convert visual input, such as images of dishes, into comprehensive culinary instructions. This process involves a fusion of advanced computer vision techniques and natural language generation algorithms, enabling a bridge between visual data and textual information. Several cutting-edge systems have been proposed to tackle the challenge of generating recipes from images. These systems employ sophisticated methodologies that leverage object detection and recognition to accurately identify ingredients and quantities within the images. By consulting extensive recipe databases, these systems generate step-by-step cooking instructions. The applications of such systems are far-reaching. They serve a wide range of users, from seasoned chefs looking for new ideas to beginners needing cooking advice. This technology introduces a novel aspect to cooking by making culinary knowledge more accessible, encouraging creativity, and improving the cooking experience. The advantages of using machine learning for recipe generation are numerous. It acts as a digital sous-chef, offering personalized recommendations tailored to user preferences and dietary needs. Additionally, the integration of user feedback and continuous refinement through advanced machine learning techniques ensures the adaptability and accuracy of these systems. Moreover, the seamless integration of such systems with smart cooking devices promises to revolutionize the way individuals approach cooking, making it more accessible, interactive, and enjoyable for everyone.

Keywords: Deep learning, recipe generation, cooking instructions, food images, culinary domain

[This article belongs to Journal of Open Source Developments (joosd)]

How to cite this article:
Om Sanjay Awari, Shivaraj Mahendra Ghangale, Janmejay Aklesh Maurya, Aishwarya Ajit Ghangale1, Keerti Kharatmol. Depiction-inspired Recipe Generator Using Deep Learning. Journal of Open Source Developments. 2024; 11(02):47-55.
How to cite this URL:
Om Sanjay Awari, Shivaraj Mahendra Ghangale, Janmejay Aklesh Maurya, Aishwarya Ajit Ghangale1, Keerti Kharatmol. Depiction-inspired Recipe Generator Using Deep Learning. Journal of Open Source Developments. 2024; 11(02):47-55. Available from: https://journals.stmjournals.com/joosd/article=2024/view=161661

References

  1. Chen JJ, Ngo CW, Chua TS. Cross-modal recipe retrieval with rich food attributes. In: Proceedings of the 25th ACM International Conference on Multimedia, Mountain View, CA, USA, October 23–27, 2017. pp. 1771–1779.
  2. Bettadapura V, Thomaz E, Parnami A, Abowd GD, Essa I. Leveraging context to support automated food recognition in restaurants. In: 2015 IEEE Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA, January 5–9, 2015. pp. 580–587.
  3. Salvador A, Drozdzal M, Giró-i-Nieto X, Romero A. Inverse cooking: recipe generation from food images. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, June 15–20, pp. 10453–10462.
  4. Teng CY, Lin YR, Adamic LA. Recipe recommendation using ingredient networks. In: Proceedings of the 4th Annual ACM Web Science Conference, June 22–24, Evanston, IL, USA, 2012. pp. 298–307.
  5. Zuo S, Xiao Y, Chang X, Wang X. Vision transformers for dense prediction: a survey. Knowledge-Based Syst. 2022; 253: 109552.
  6. Salvador A, Drozdzal M, Giró-i-Nieto X, Romero A. Inverse cooking: recipe generation from food images. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, June 15–20, 2019. pp. 10453–10462.
  7. Han F, Guerrero R, Pavlovic V. CookGAN: meal image synthesis from ingredients. In: 2020 IEEE/CVF Winter Conference on Applications of Computer Vision, Snowmass, CO, USA, March 1–5, 2020. pp. 1450–1458.
  8. Fujita J, Sato M, Nobuhara H. Model for cooking recipe generation using reinforcement learning. In: 2021 IEEE 37th International Conference on Data Engineering Workshops (ICDEW), Chania, Greece, April 19–22, 2021. pp. 1–4.
  9. Salvador A, Hynes N, Aytar Y, Marin J, Ofli F, Weber I, Torralba A. Learning cross-modal embeddings for cooking recipes and food images. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, July 21–26, 2017. pp. 3020–3028.
  10. Chhikara P, Chaurasia D, Jiang Y, Masur O, Ilievski F. Fire: food image to recipe generation. In: 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA, January 3–8, 2024. pp. 8184–8194.
  11. Wang L, Li Q, Li N, Dong G, Yang Y. Substructure similarity measurement in Chinese recipes. In: Proceedings of the 17th International Conference on World Wide Web, Beijing, China, April 21–25, 2008. pp. 979–988.
  12. Pan S, Dai L, Hou X, Li H, Sheng B. ChefGAN: Food image generation from recipes. In: Proceedings of the 28th ACM International Conference on Multimedia, Seattle, WA, USA, October 12–16, 2020. pp. 4244–4252.
  13. Salvador A, Gundogdu E, Bazzani L, Donoser M. Revamping cross-modal recipe retrieval with hierarchical transformers and self-supervised learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA, June 20–25, 2021. pp. 15475–15484.
  14. Chen J, Ngo CW. Deep-based ingredient recognition for cooking recipe retrieval. In: Proceedings of the 24th ACM International Conference on Multimedia, Amsterdam, The Netherlands, October 15–19, 2016. pp. 32–41.
  15. Chen J, Pang L, Ngo CW. Cross-modal recipe retrieval: how to cook this dish? In: Amsaleg L, Guðmundsson G, Gurrin C, Jónsson B, Satoh S, editors. MultiMedia Modeling: 23rd International Conference, MMM 2017, Reykjavik, Iceland, January 4–6, 2017, Proceedings, Part I 23. Cham, Switzerland: Springer International Publishing; 2017. pp. 588–600.
  16. Wang X, Kumar D, Thome N, Cord M, Precioso F. Recipe recognition with large multimodal food dataset. In: 2015 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), Turin, Italy, June 29–July 3, 2015. pp. 1–6.
  17. Zhu B, Ngo CW, Chen J, Hao Y. R2GAN: cross-modal recipe retrieval with generative adversarial network. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, June 15–20, 2019. pp. 11477–11486.
  18. Wang S, Gao H, Zhu Y, Zhang W, Chen Y. A food dish image generation framework based on progressive growing GANs. In: Wang X, Gao H, Iqbal M, Min G, editors. Collaborative Computing: Networking, Applications and Worksharing: 15th EAI International Conference, CollaborateCom 2019, London, UK, August 19–22, 2019, Proceedings 15. Cham, Switzerland: Springer International Publishing. pp. 323–333.
  19. Martinel N, Foresti GL, Micheloni C. Wide-slice residual networks for food recognition. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA, March 12–15, 2018. pp. 567–576.
  20. Salvador A, Hynes N, Aytar Y, Marin J, Ofli F, Weber I, Torralba A. Learning cross-modal embeddings for cooking recipes and food images. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, July 21–26, 2017. pp. 3020–3028.
  21. Zhang XJ, Lu YF, Zhang SH. Multi-task learning for food identification and analysis with deep convolutional neural networks. J Computer Sci Technol. 2016; 31 (3): 489–500.

Regular Issue Subscription Review Article
Volume 11
Issue 02
Received 28/06/2024
Accepted 31/07/2024
Published 07/08/2024