An End-to-End Food Image Analysis System

• URL: http://arxiv.org/abs/2102.00645v1
• Date: Mon, 1 Feb 2021 05:36:20 GMT
• Title: An End-to-End Food Image Analysis System
• Authors: Jiangpeng He, Runyu Mao, Zeman Shao, Janine L. Wright, Deborah A. Kerr, Carol J. Boushey and Fengqing Zhu
• Abstract summary: We propose an image-based food analysis framework that integrates food localization, classification and portion size estimation. Our proposed framework is end-to-end, i.e., the input can be an arbitrary food image containing multiple food items. Our framework is evaluated on a real life food image dataset collected from a nutrition feeding study.
• Score: 8.622335099019214
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern deep learning techniques have enabled advances in image-based dietary assessment such as food recognition and food portion size estimation. Valuable information on the types of foods and the amount consumed are crucial for prevention of many chronic diseases. However, existing methods for automated image-based food analysis are neither end-to-end nor are capable of processing multiple tasks (e.g., recognition and portion estimation) together, making it difficult to apply to real life applications. In this paper, we propose an image-based food analysis framework that integrates food localization, classification and portion size estimation. Our proposed framework is end-to-end, i.e., the input can be an arbitrary food image containing multiple food items and our system can localize each single food item with its corresponding predicted food type and portion size. We also improve the single food portion estimation by consolidating localization results with a food energy distribution map obtained by conditional GAN to generate a four-channel RGB-Distribution image. Our end-to-end framework is evaluated on a real life food image dataset collected from a nutrition feeding study.

Related papers

• Shape-Preserving Generation of Food Images for Automatic Dietary Assessment [1.602820210496921]
  We present a simple GAN-based neural network architecture for conditional food image generation. The shapes of the food and container in the generated images closely resemble those in the reference input image.
  arXiv  Detail & Related papers  (2024-08-23T20:18:51Z)
• 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)
• An End-to-end Food Portion Estimation Framework Based on Shape Reconstruction from Monocular Image [7.380382380564532]
  We propose an end-to-end deep learning framework for food energy estimation from a monocular image through 3D shape reconstruction. Our method is evaluated on a publicly available food image dataset Nutrition5k, resulting a Mean Absolute Error (MAE) of 40.05 kCal and Mean Absolute Percentage Error (MAPE) of 11.47% for food energy estimation.
  arXiv  Detail & Related papers  (2023-08-03T15:17:24Z)
• 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 Large-Scale Benchmark for Food Image Segmentation [62.28029856051079]
  We build a new food image dataset FoodSeg103 (and its extension FoodSeg154) containing 9,490 images. We annotate these images with 154 ingredient classes and each image has an average of 6 ingredient labels and pixel-wise masks. We propose a multi-modality pre-training approach called ReLeM that explicitly equips a segmentation model with rich and semantic food knowledge.
  arXiv  Detail & Related papers  (2021-05-12T03:00:07Z)
• 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)
• Multi-Task Image-Based Dietary Assessment for Food Recognition and Portion Size Estimation [6.603050343996914]
  We propose an end-to-end multi-task framework that can achieve both food classification and food portion size estimation. Our results outperforms the baseline methods for both classification accuracy and mean absolute error for portion estimation.
  arXiv  Detail & Related papers  (2020-04-27T21:35:07Z)
• Cross-Modal Food Retrieval: Learning a Joint Embedding of Food Images and Recipes with Semantic Consistency and Attention Mechanism [70.85894675131624]
  We learn an embedding of images and recipes in a common feature space, such that the corresponding image-recipe embeddings lie close to one another. We propose Semantic-Consistent and Attention-based Networks (SCAN), which regularize the embeddings of the two modalities through aligning output semantic probabilities. We show that we can outperform several state-of-the-art cross-modal retrieval strategies for food images and cooking recipes by a significant margin.
  arXiv  Detail & Related papers  (2020-03-09T07:41:17Z)

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.