DeepFood: Deep Learning-Based Food Image Recognition for Computer-Aided Dietary Assessment

By Chang Liu, Yu Cao, Yan Luo, Guanling Chen, Vinod M. Vokkarane, Yunsheng Ma. 2016

284 citations recorded by the source corpus.

Worldwide, in 2014, more than 1.9 billion adults, 18 years and older, were overweight. Of these, over 600 million were obese. Accurately documenting dietary caloric intake is crucial to manage weight loss, but also presents challenges because most of the current methods for dietary assessment must rely on memory to recall foods eaten. The ultimate goal of our research is to develop computer-aided technical solutions to enhance and improve the accuracy of current measurements of dietary intake. Our proposed system in this paper aims to improve the accuracy of dietary assessment by analyzing the food images captured by mobile devices e.g., smartphone. The key technique innovation in this paper is the deep learning-based food image recognition algorithms. Substantial research has demonstrated that digital imaging accurately estimates dietary intake in many environments and it has many advantages over other methods. However, how to derive the food information e.g., food type and portion size from food image effectively and efficiently remains a challenging and open research problem. We propose a new Convolutional Neural Network CNN-based food image recognition algorithm to address this problem. We applied our proposed approach to two real-world food image data sets UEC-256 and Food-101 and achieved impressive results. To the best of our knowledge, these results outperformed all other reported work using these two data sets. Our experiments have demonstrated that the proposed approach is a promising solution for addressing the food image recognition problem. Our future work includes further improving the performance of the algorithms and integrating our system into a real-world mobile and cloud computing-based system to enhance the accuracy of current measurements of dietary intake.

Subfields: Biomedical Engineering, Plant Science, Public Health, Environmental and Occupational Health

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Citations in the corpus

Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, background): “[82], [128] developed a system to recognize food images and their relevant information, such as types and portion”
Multi-task Self-Supervised Learning for Human Activity Detection (2019, methodology): “5 RELATED WORK Deep learning methods have been successfully used in several applications of ubiquitous computing, pervasive intelligence, health, and well-being [17, 21, 38, 56, 60, 73] and eliminate the need of hand-craed feature engineering.”
Inverse Cooking: Recipe Generation From Food Images (2018, background): “As a result, there is currently a vast literature in computer vision dealing with a variety of food related tasks, with special focus in image classification [26, 39, 38, 33, 6, 24, 30, 60, 16, 17].”
Recipe1M: A Dataset for Learning Cross-Modal Embeddings for Cooking Recipes and Food Images (2018, background): “Even with the impetus for food image categorization, subsequent work by [9], [10] and [11] could only improve this result to 77.4%, 79% and 80.9%, respectively, which indicates that the size of the dataset may be the limiting factor.”
Convolutional neural network: a review of models, methodologies and applications to object detection (2019, background): “ResNet 2018 Chang Liu [100] Test accuracy 81.”
A Survey on Food Computing (2018, background): “2016] AlexNet Food recognition [Liu et al. 2016] Inception Food recognition [Hassannejad et al.”
Learning Cross-Modal Embeddings for Cooking Recipes and Food Images (2017, background): “Existing work, however, has focused largely on the use of medium-scale datasets for performing categorization [1, 8, 16, 13].”
A New Deep Learning-Based Food Recognition System for Dietary Assessment on An Edge Computing Service Infrastructure (2018, result): “Again, if we compare the results in Table 4 with the results in our previous publication [60], we can find that, due to the new system and algorithms in this paper, both the accuracy and the time complexity have been slightly reduced.”