Abstract
Dietary studies showed that dietary problems such as obesity are associated with other chronic diseases, including hypertension, irregular blood sugar levels, and increased risk of heart attacks. The primary cause of these problems is poor lifestyle choices and unhealthy dietary habits, which are manageable using interactive mHealth apps. However, traditional dietary monitoring systems using manual food logging suffer from imprecision, underreporting, time consumption, and low adherence. Recent dietary monitoring systems tackle these challenges by automatic assessment of dietary intake through machine learning methods. This survey discusses the best-performing methodologies that have been developed so far for automatic food recognition and volume estimation. Firstly, the paper presented the rationale of visual-based methods for food recognition. Then, the core of the study is the presentation, discussion, and evaluation of these methods based on popular food image databases. In this context, this study discusses the mobile applications that are implementing these methods for automatic food logging. Our findings indicate that around 66.7% of surveyed studies use visual features from deep neural networks for food recognition. Similarly, all surveyed studies employed a variant of convolutional neural networks (CNN) for ingredient recognition due to recent research interest. Finally, this survey ends with a discussion of potential applications of food image analysis, existing research gaps, and open issues of this research area. Learning from unlabeled image datasets in an unsupervised manner, catastrophic forgetting during continual learning, and improving model transparency using explainable AI are potential areas of interest for future studies.
Highlights
Despite recent advancements in medicine, the number of people affected by chronic diseases is still large [1]
Our findings indicate that the ultimate performance of traditional and deep visual techniques depends on the type of dataset used
UECFOOD-256 (25,088 images and 256 classes) and UECFOOD-100 (14,361 images and classes of food) are Japanese food datasets consisting of Japanese food images captured by users, whereas Food-101(101,000 images and classes) is an American fast food dataset containing images crawled from several websites
Summary
Despite recent advancements in medicine, the number of people affected by chronic diseases is still large [1]. This rate is primarily due to their unhealthy lifestyles and irregular eating patterns. Obesity and weight issues are becoming increasingly common around the globe. The main reported obesity issues are in developed and middle-income countries. In 2016, 1.9 billion adults 18 years and older were overweight, while 650 million were obese. Children are becoming affected by obesity at an alarming rate. According to World Health Organization (WHO), over 340 million children and adolescents between 5 and 19 years were overweight or obese [6]
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