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Dietary studies showed that dietary-related problem such as obesity isassociated with other chronic diseases like hypertension, irregular blood sugarlevels, and increased risk of heart attacks. The primary cause of theseproblems is poor lifestyle choices and unhealthy dietary habits, which aremanageable using interactive mHealth apps. However, traditional dietarymonitoring systems using manual food logging suffer from imprecision,underreporting, time consumption, and low adherence. Recent dietary monitoringsystems tackle these challenges by automatic assessment of dietary intakethrough machine learning methods. This survey discusses the most performingmethodologies that have been developed so far for automatic food recognitionand volume estimation. First, we will present the rationale of visual-basedmethods for food recognition. The core of the paper is the presentation,discussion and evaluation of these methods on popular food image databases.Following that, we discussed the mobile applications that are implementingthese methods. The survey ends with a discussion of research gaps and openissues in this area.

A Comprehensive Survey of Image-Based Food Recognition and Volume Estimation Methods for Dietary Assessment