ArticleNutrition research and practice2019
The development of food image detection and recognition model of Korean food for mobile dietary management.
Article in Nutrition research and practice, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 3 of them syntheses that pooled it.
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Who cites it
10 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Empowering Diabetics: Advancements in Smartphone-Based Food Classification, Volume Measurement, and Nutritional Estimation.Sensors (Basel, Switzerland) · 2024Pooled it
- Applying Image-Based Food-Recognition Systems on Dietary Assessment: A Systematic Review.Advances in nutrition (Bethesda, Md.) · 2022Pooled it
- Thought on Food: A Systematic Review of Current Approaches and Challenges for Food Intake Detection.Sensors (Basel, Switzerland) · 2022Pooled it
- Nutritional assessment system integrating semantic segmentation and point cloud modeling techniques.Scientific reports · 2025Article
- Gluten identification from food images using advanced deep learning and transfer learning methods.Journal of food science and technology · 2025Article
- Vision-Based Methods for Food and Fluid Intake Monitoring: A Literature Review.Sensors (Basel, Switzerland) · 2023Review
- Multimedia Data-Based Mobile Applications for Dietary Assessment.Journal of diabetes science and technology · 2023Review
- Mobile Computer Vision-Based Applications for Food Recognition and Volume and Calorific Estimation: A Systematic Review.Healthcare (Basel, Switzerland) · 2022Review
- Eliminate the hardware: Mobile terminals-oriented food recognition and weight estimation system.Frontiers in nutrition · 2022Article
- Prediction Model of Aryl Hydrocarbon Receptor Activation by a Novel QSAR Approach, DeepSnap-Deep Learning.Molecules (Basel, Switzerland) · 2020Article
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND/
objectivesThe aim of this study was to develop Korean food image detection and recognition model for use in mobile devices for accurate estimation of dietary intake. SUBJECTS/
methodsWe collected food images by taking pictures or by searching web images and built an image dataset for use in training a complex recognition model for Korean food. Augmentation techniques were performed in order to increase the dataset size. The dataset for training contained more than 92,000 images categorized into 23 groups of Korean food. All images were down-sampled to a fixed resolution of 150 × 150 and then randomly divided into training and testing groups at a ratio of 3:1, resulting in 69,000 training images and 23,000 test images. We used a Deep Convolutional Neural Network (DCNN) for the complex recognition model and compared the results with those of other networks: AlexNet, GoogLeNet, Very Deep Convolutional Neural Network, VGG and ResNet, for large-scale image recognition.
resultsOur complex food recognition model, K-foodNet, had higher test accuracy (91.3%) and faster recognition time (0.4 ms) than those of the other networks.
conclusionThe results showed that K-foodNet achieved better performance in detecting and recognizing Korean food compared to other state-of-the-art models.
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