Image-based methods for dietary assessment: a survey

Accurate dietary assessment plays a crucial role in monitoring dietary behaviour, and in facilitating healthcare professionals with their formulation of personalized diets and exercise plans. This, in turn, allows for targeted interventions to prevent chronic conditions like obesity, diabetes, and cardiovascular disease. Image-based computer vision and artificial intelligence techniques have been developed to support dietary assessment by classifying food items, estimating portion sizes, and providing nutrient information. However, the accuracy of food image detection and nutrition estimation poses a challenge, due to the varying shape and appearance of food items during preparation, cooking, and consumption. This survey offers a comprehensive review of state-of-the-art image-based methods for food identification in dietary assessment. It covers various aspects, including food image datasets, food detection algorithms, and approaches for estimating food volume and nutrition for popular foods. The performance, feasibility, challenges, and problems associated with each subtask of automating dietary assessment are outlined and summarized.