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Foodnet: multi-scale and label dependency learning-based multi-task network for food and ingredient recognition

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Abstract

Image-based food pattern classification poses challenges of non-fixed spatial distribution and ingredient occlusion for mainstream computer vision algorithms. However, most current approaches classify food and ingredients by directly extracting abstract features of the entire image through a convolutional neural network (CNN), ignoring the relationship between food and ingredients and ingredient occlusion problem. To address these issues mentioned, we propose a FoodNet for both food and ingredient recognition, which uses a multi-task structure with a multi-scale relationship learning module (MSRL) and a label dependency learning module (LDL). As ingredients normally co-occur in an image, we present the LDL to use the dependency of ingredient to alleviate the occlusion problem of ingredient. MSRL aggregates multi-scale information of food and ingredients, then uses two relational matrixs to model the food-ingredient matching relationship to obtain richer feature representation. The experimental results show that FoodNet can achieve good performance on the Vireo Food-172 and UEC Food-100 datasets. It is worth noting that it reaches the most state-of-the-art level in terms of ingredient recognition in the Vireo Food-172 and UECFood-100.The source code will be made available at https://github.com/visipaper/FoodNet.

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Data Availability

The datasets VireoFood-172 and UEC Food-100 used to train and evaluate the neural networks are publicly available at http://vireo.cs.cityu.edu.hk/VireoFood172/ and http://foodcam.mobi/dataset.html.

Notes

  1. http://pytorch.org/.

  2. https://scikit-learn.org/stable/.

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Author information

Author notes

  1. Feng Shuang, Zhouxian Lu and Yong Li are shared the equal contributions and shared the co-first author.

Authors and Affiliations

  1. The Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment, School of Electrical Engineering, Guangxi University, Nanning, 530000, China

    Feng Shuang, Zhouxian Lu, Yong Li, Chao Han, Xia Gu & Shidi Wei

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Correspondence to Yong Li.

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Appendix A: FoodNet structure information

Appendix A: FoodNet structure information

The detailed structure of FoodNet is shown in Tables 12, 13.

Table 12 Structure details of the FoodNet based on ResNet50
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Table 13 Structure details of the FoodNet based on DenseNet161
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Shuang, F., Lu, Z., Li, Y. et al. Foodnet: multi-scale and label dependency learning-based multi-task network for food and ingredient recognition. Neural Comput & Applic 36, 4485–4501 (2024). https://doi.org/10.1007/s00521-023-09349-4

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