Vision-Based Food Analysis for Automatic Dietary Assessment

• URL: http://arxiv.org/abs/2108.02947v1
• Date: Fri, 6 Aug 2021 05:46:01 GMT
• Title: Vision-Based Food Analysis for Automatic Dietary Assessment
• Authors: Wei Wang, Weiqing Min, Tianhao Li, Xiaoxiao Dong, Haisheng Li and Shuqiang Jiang
• Abstract summary: This review presents one unified Vision-Based Dietary Assessment (VBDA) framework, which generally consists of three stages: food image analysis, volume estimation and nutrient derivation. Deep learning makes VBDA gradually move to an end-to-end implementation, which applies food images to a single network to directly estimate the nutrition.
• Score: 49.32348549508578
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Background: Maintaining a healthy diet is vital to avoid health-related issues, e.g., undernutrition, obesity and many non-communicable diseases. An indispensable part of the health diet is dietary assessment. Traditional manual recording methods are burdensome and contain substantial biases and errors. Recent advances in Artificial Intelligence, especially computer vision technologies, have made it possible to develop automatic dietary assessment solutions, which are more convenient, less time-consuming and even more accurate to monitor daily food intake. Scope and approach: This review presents one unified Vision-Based Dietary Assessment (VBDA) framework, which generally consists of three stages: food image analysis, volume estimation and nutrient derivation. Vision-based food analysis methods, including food recognition, detection and segmentation, are systematically summarized, and methods of volume estimation and nutrient derivation are also given. The prosperity of deep learning makes VBDA gradually move to an end-to-end implementation, which applies food images to a single network to directly estimate the nutrition. The recently proposed end-to-end methods are also discussed. We further analyze existing dietary assessment datasets, indicating that one large-scale benchmark is urgently needed, and finally highlight key challenges and future trends for VBDA. Key findings and conclusions: After thorough exploration, we find that multi-task end-to-end deep learning approaches are one important trend of VBDA. Despite considerable research progress, many challenges remain for VBDA due to the meal complexity. We also provide the latest ideas for future development of VBDA, e.g., fine-grained food analysis and accurate volume estimation. This survey aims to encourage researchers to propose more practical solutions for VBDA.

Related papers

• NutritionVerse-Direct: Exploring Deep Neural Networks for Multitask Nutrition Prediction from Food Images [63.314702537010355]
  Self-reporting methods are often inaccurate and suffer from substantial bias. Recent work has explored using computer vision prediction systems to predict nutritional information from food images. This paper aims to enhance the efficacy of dietary intake estimation by leveraging various neural network architectures.
  arXiv  Detail & Related papers  (2024-05-13T14:56:55Z)
• How Much You Ate? Food Portion Estimation on Spoons [63.611551981684244]
  Current image-based food portion estimation algorithms assume that users take images of their meals one or two times. We introduce an innovative solution that utilizes stationary user-facing cameras to track food items on utensils. The system is reliable for estimation of nutritional content of liquid-solid heterogeneous mixtures such as soups and stews.
  arXiv  Detail & Related papers  (2024-05-12T00:16:02Z)
• DPF-Nutrition: Food Nutrition Estimation via Depth Prediction and Fusion [0.8579795118452238]
  DPF-Nutrition is an end-to-end nutrition estimation method using monocular images. In DPF-Nutrition, we introduced a depth prediction module to generate depth maps, thereby improving the accuracy of food portion estimation. We also designed an RGB-D fusion module that combined monocular images with the predicted depth information, resulting in better performance for nutrition estimation.
  arXiv  Detail & Related papers  (2023-10-18T04:23:05Z)
• NutritionVerse: Empirical Study of Various Dietary Intake Estimation Approaches [59.38343165508926]
  Accurate dietary intake estimation is critical for informing policies and programs to support healthy eating. Recent work has focused on using computer vision and machine learning to automatically estimate dietary intake from food images. We introduce NutritionVerse- Synth, the first large-scale dataset of 84,984 synthetic 2D food images with associated dietary information. We also collect a real image dataset, NutritionVerse-Real, containing 889 images of 251 dishes to evaluate realism.
  arXiv  Detail & Related papers  (2023-09-14T13:29:41Z)
• Towards the Creation of a Nutrition and Food Group Based Image Database [58.429385707376554]
  We propose a framework to create a nutrition and food group based image database. We design a protocol for linking food group based food codes in the U.S. Department of Agriculture's (USDA) Food and Nutrient Database for Dietary Studies (FNDDS) Our proposed method is used to build a nutrition and food group based image database including 16,114 food datasets.
  arXiv  Detail & Related papers  (2022-06-05T02:41:44Z)
• Towards Building a Food Knowledge Graph for Internet of Food [66.57235827087092]
  We review the evolution of food knowledge organization, from food classification to food to food knowledge graphs. Food knowledge graphs play an important role in food search and Question Answering (QA), personalized dietary recommendation, food analysis and visualization. Future directions for food knowledge graphs cover several fields such as multimodal food knowledge graphs and food intelligence.
  arXiv  Detail & Related papers  (2021-07-13T06:26:53Z)
• A Review of the Vision-based Approaches for Dietary Assessment [4.347952928399708]
  Dietary-related problems such as obesity are a growing concern in todays modern world. Computer-based food recognition offers automatic visual-based methods to assess dietary intake.
  arXiv  Detail & Related papers  (2021-06-21T06:30:06Z)
• An Intelligent Passive Food Intake Assessment System with Egocentric Cameras [14.067860492694251]
  Malnutrition is a major public health concern in low-and-middle-income countries (LMICs) We propose to implement an intelligent passive food intake assessment system via egocentric cameras. Our method is able to reliably monitor food intake and give feedback on users' eating behaviour.
  arXiv  Detail & Related papers  (2021-05-07T09:47:51Z)
• An Artificial Intelligence-Based System to Assess Nutrient Intake for Hospitalised Patients [4.048427587958764]
  Regular monitoring of nutrient intake in hospitalised patients plays a critical role in reducing the risk of disease-related malnutrition. We propose a novel system based on artificial intelligence (AI) to accurately estimate nutrient intake.
  arXiv  Detail & Related papers  (2020-03-18T15:28:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.