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

TL;DR

This survey reviews recent image-based food recognition and volume estimation techniques for dietary assessment, highlighting their methodologies, applications, and current research gaps to improve health monitoring.

Contribution

It provides a comprehensive overview of state-of-the-art visual-based methods for food recognition and volume estimation, including evaluations on popular datasets and mobile app implementations.

Findings

Recent methods achieve high accuracy in food recognition

Volume estimation techniques vary in precision and complexity

Identifies key research gaps and open challenges

Abstract

Dietary studies showed that dietary-related problem such as obesity is associated with other chronic diseases like 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 most performing methodologies that have been developed so far for automatic food recognition and volume estimation. First, we will present the rationale of visual-based methods 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 implementing these methods. The survey ends with a discussion of research gaps and open issues in this area.