NutriTransform: Estimating Nutritional Information From Online Food Posts

• URL: http://arxiv.org/abs/2503.04755v1
• Date: Sun, 09 Feb 2025 10:33:29 GMT
• Title: NutriTransform: Estimating Nutritional Information From Online Food Posts
• Authors: Thorsten Ruprechter, Marion Garaus, Ivo Ponocny, Denis Helic,
• Abstract summary: We present an efficient and straightforward approach to approximating macro-nutrients based solely on the titles of food posts.<n>We evaluate the approach on a labeled food dataset, demonstrating its effectiveness, and apply it to over 500,000 real-world posts from Reddit's popular /r/food subreddit.<n>This work lays a foundation for researchers and practitioners aiming to estimate caloric and nutritional content using only text data.
• Score: 0.046873264197900916
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deriving nutritional information from online food posts is challenging, particularly when users do not explicitly log the macro-nutrients of a shared meal. In this work, we present an efficient and straightforward approach to approximating macro-nutrients based solely on the titles of food posts. Our method combines a public food database from the U.S. Department of Agriculture with advanced text embedding techniques. We evaluate the approach on a labeled food dataset, demonstrating its effectiveness, and apply it to over 500,000 real-world posts from Reddit's popular /r/food subreddit to uncover trends in food-sharing behavior based on the estimated macro-nutrient content. Altogether, this work lays a foundation for researchers and practitioners aiming to estimate caloric and nutritional content using only text data.

Related papers

• Reddit's Appetite: Predicting User Engagement with Nutritional Content [0.051205673783866146]
  We study the impact of nutritional content on user engagement in food-related posts on Reddit.<n>Our results provide valuable insights for the design of more engaging online initiatives aimed at encouraging healthy eating habits.
  arXiv  Detail & Related papers  (2025-02-11T08:54:53Z)
• 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)
• 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)
• Muti-Stage Hierarchical Food Classification [9.013592803864086]
  We propose a multi-stage hierarchical framework for food item classification by iteratively clustering and merging food items during the training process. Our method is evaluated on VFN-nutrient dataset and achieve promising results compared with existing work in terms of both food type and food item classification.
  arXiv  Detail & Related papers  (2023-09-03T04:45:44Z)
• NutritionVerse-3D: A 3D Food Model Dataset for Nutritional Intake Estimation [65.47310907481042]
  One in four older adults are malnourished. Machine learning and computer vision show promise of automated nutrition tracking methods of food. NutritionVerse-3D is a large-scale high-resolution dataset of 105 3D food models.
  arXiv  Detail & Related papers  (2023-04-12T05:27:30Z)
• 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)
• Nutrition5k: Towards Automatic Nutritional Understanding of Generic Food [8.597152169571057]
  We introduce Nutrition5k, a novel dataset of 5k diverse, real world food dishes with corresponding video streams, depth images, component weights, and high accuracy nutritional content annotation. We demonstrate the potential of this dataset by training a computer vision algorithm capable of predicting the caloric and macronutrient values of a complex, real world dish at an accuracy that outperforms professional nutritionists.
  arXiv  Detail & Related papers  (2021-03-04T22:59:22Z)

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.