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논문 기본 정보

자료유형
학술저널
저자정보
Seon-Joo Park (Gachon University) Akmaljon Palvanov (Gachon University) Chang-Ho Lee (Korea Food Research Institute) Nanoom Jeong (Gachon University) Young-Im Cho (Gachon University) Hae-Jeung Lee (Gachon University)
저널정보
대한지역사회영양학회 Nutrition Research and Practice Nutrition Research and Practice Vol.13 No.6
발행연도
2019.12
수록면
521 - 528 (8page)

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초록· 키워드

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BACKGROUND/OBJECTIVES: The 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.
MATERIALS/METHODS: We 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.
RESULTS: Our complex food recognition model, K-foodNet, had higher test accuracy (91.3%) and faster recognition time (0.4 ㎳) than those of the other networks.
CONCLUSION: The 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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INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
REFERENCES

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