The DOI matches its Crossref record; 223 of the 223 references it cites were found in a registry or an index.
Deep learning in food category recognition
Open accessAbstract
Integrating artificial intelligence with food category recognition has been a field of interest for research for the past few decades. It is potentially one of the next steps in revolutionizing human interaction with food. The modern advent of big data and the development of data-oriented fields like deep learning have provided advancements in food category recognition. With increasing computational power and ever-larger food datasets, the approach's potential has yet to be realized. This survey provides an overview of methods that can be applied to various food category recognition tasks, including detecting type, ingredients, quality, and quantity. We survey the core components for constructing a machine learning system for food category recognition, including datasets, data augmentation, hand-crafted feature extraction, and machine learning algorithms. We place a particular focus on the field of deep learning, including the utilization of convolutional neural networks, transfer learning, and semi-supervised learning. We provide an overview of relevant studies to promote further developments in food category recognition for research and industrial applications.
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Metadata
- Authors
- Zhang, Y., Deng, L., Zhu, H., Wang, W., Ren, Z., Zhou, Q., Lu, S., Sun, S., Zhu, Z., Gorriz, J. M., & Wang, S.
- Venue
- Information Fusion
- Publisher
- Elsevier BV
- Published
- 2023-10
- Volume
- 98
- Pages
- 101859
- ISSN
- 1566-2535 (ISSN-L)
- OpenAlex
- W4378619666
- Cited by
- 357
- References
- 223
- FWCI
- 32.46
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Zhang, Y., Deng, L., Zhu, H., Wang, W., Ren, Z., Zhou, Q., Lu, S., Sun, S., Zhu, Z., Gorriz, J. M., & Wang, S. (2023). Deep learning in food category recognition. Information Fusion. https://doi.org/10.1016/j.inffus.2023.101859
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