Image-Based Dietary Assessment: A Healthy Eating Plate Estimation System

• URL: http://arxiv.org/abs/2403.01310v1
• Date: Sat, 2 Mar 2024 21:01:01 GMT
• Title: Image-Based Dietary Assessment: A Healthy Eating Plate Estimation System
• Authors: Assylzhan Izbassar and Pakizar Shamoi
• Abstract summary: The nutritional quality of diets has significantly deteriorated over the past two to three decades, a decline often underestimated by the people. This paper introduces an innovative Image-Based Dietary Assessment system aimed at evaluating the healthiness of meals through image analysis. Our system employs advanced image segmentation and classification techniques to analyze food items on a plate, assess their proportions, and calculate meal adherence to Harvard's healthy eating recommendations.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The nutritional quality of diets has significantly deteriorated over the past two to three decades, a decline often underestimated by the people. This deterioration, coupled with a hectic lifestyle, has contributed to escalating health concerns. Recognizing this issue, researchers at Harvard have advocated for a balanced nutritional plate model to promote health. Inspired by this research, our paper introduces an innovative Image-Based Dietary Assessment system aimed at evaluating the healthiness of meals through image analysis. Our system employs advanced image segmentation and classification techniques to analyze food items on a plate, assess their proportions, and calculate meal adherence to Harvard's healthy eating recommendations. This approach leverages machine learning and nutritional science to empower individuals with actionable insights for healthier eating choices. Our four-step framework involves segmenting the image, classifying the items, conducting a nutritional assessment based on the Harvard Healthy Eating Plate research, and offering tailored recommendations. The prototype system has shown promising results in promoting healthier eating habits by providing an accessible, evidence-based tool for dietary assessment.

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)
• NutritionVerse-Real: An Open Access Manually Collected 2D Food Scene Dataset for Dietary Intake Estimation [68.49526750115429]
  We introduce NutritionVerse-Real, an open access manually collected 2D food scene dataset for dietary intake estimation. The NutritionVerse-Real dataset was created by manually collecting images of food scenes in real life, measuring the weight of every ingredient and computing the associated dietary content of each dish.
  arXiv  Detail & Related papers  (2023-11-20T11:05:20Z)
• 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)
• Vision-Based Food Analysis for Automatic Dietary Assessment [49.32348549508578]
  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.
  arXiv  Detail & Related papers  (2021-08-06T05:46:01Z)
• 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)
• Saliency-Aware Class-Agnostic Food Image Segmentation [10.664526852464812]
  We propose a class-agnostic food image segmentation method. Using information from both the before and after eating images, we can segment food images by finding the salient missing objects. Our method is validated on food images collected from a dietary study.
  arXiv  Detail & Related papers  (2021-02-13T08:05:19Z)
• MyFood: A Food Segmentation and Classification System to Aid Nutritional Monitoring [1.5469452301122173]
  The absence of food monitoring has contributed significantly to the increase in the population's weight. Some solutions have been proposed in computer vision to recognize food images, but few are specialized in nutritional monitoring. This work presents the development of an intelligent system that classifies and segments food presented in images to help the automatic monitoring of user diet and nutritional intake.
  arXiv  Detail & Related papers  (2020-12-05T17:40:05Z)

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.