The development of food image detection and recognition model of Korean food for mobile dietary management

Park, Seon-Joo; Palvanov, Akmaljon; Lee, Chang-Ho; Jeong, Nanoom; Cho, Young Im; Lee, Hae‐Jeung

doi:10.4162/nrp.2019.13.6.521

Cited by 38 publications

(18 citation statements)

References 23 publications

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“…Images of Korean food are resized and divided into training and testing groups. Deep convolutional neural network is used for the food recognition, and the results are tested with other models like AlexNet, GoogLeNet, VGG and ResNet [19].…”

Section: Related Workmentioning

confidence: 99%

Recognition of food type and calorie estimation using neural network

et al. 2021

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Across the globe, health cognizant among the people is increasing and everyone wants to maintain a healthy and normal life. But due to the fast moving world, obesity and other related issue becomes the major health problem among the human beings. According to medical experts, a person is defined as obese when their BMI is greater than 30 kg/m 2. Obesity leads to many diseases like high cholesterol, liver failure, breathing issues, heart problems, diabetes and sometimes cancer. By eating healthy foods with high nutrition and low calorie values, we can control the obesity among the people. Human cannot control their appetite and have the nature of eating food which they like the most which leads to obesity. Many people have the difficulty in choosing the food items that have good nutrient and low calorific values. If a system can help the people and give them suggestions about the food and its calorific values, we can find a solution for this obesity problem. In this paper, identifying the food type and its calorific value estimation is done using multilayer perceptron model and the results are discussed. From the mixed food items, region of interest is selected from which the features are extracted. Extracted features are fed as the input to the MLP. Based on the food volume, the calories present in the food are calculated. Implementation of the algorithm is done in MATLAB environment for fruits and food items. The results showed that the level of detection of food item and accuracy of estimation of calorific level was acceptable.

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Section: Related Workmentioning

confidence: 99%

Recognition of food type and calorie estimation using neural network

et al. 2021

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“…anks to the development of deep learning architectures [7][8][9][10][11][12], the availability of massive data samples [13][14][15][16][17], and the upgrade of computational hardware, end-to-end approaches help boost the performance of dish recognition [18][19][20][21][22][23][24][25][26][27]. Wu exploits the semantic relationship among finegrained food categories, and a multitask learning procedure is added to a convolutional neural network (CNN) [27].…”

Section: Introductionmentioning

confidence: 99%

Accurate Real-Life Chinese Dish Recognition

Lan

Wan

Pang

et al. 2022

International Transactions on Electrical Energy Systems

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Deep learning is a new research direction in the field of machine learning, which was introduced into machine learning to bring it closer to its original goal. Accurate dish recognition becomes increasingly important in the multimedia community since it can help cuisine recommendation, calorie management, service improvement, and other food computing tasks. Many novel approaches have been developed on web recipes and menu pictures, while few are concerned real-life dish image analysis. In this study, a deep learning-based prototype system is deployed in a Chinese canteen, and 28 dish types, 16,904 images, and 45,061 instances have been collected. Specifically, in the prototype system, three practical issues are explored, including the backbone network selection, the training strategy determination, and the minimum number of samples for model upgrading. Experimental results suggest that fine-tuned Faster-RCNN can serve as the backbone network of the prototype system since it outperforms the other four fine-tuned networks on dish recognition (accuracy, 98.10%; recall, 97.20%; MAP (mean average precession), 98.30%) and satisfies real-time requirement (0.15 second per image). Meanwhile, the transferred backbone network achieves superior results (MAP, 96.48%) over the same architecture trained from image scratches (MAP, 87.84%). On model upgrading, a good (MAP, 91.34%) to better (MAP, 96.48%) outcome is obtained when the training size is increased from 50 to 200 samples per dish type, and 150 and more instances should be annotated if a new dish type is added to the system's recognition list. Conclusively, the real-life deployment and evaluation of the prototype system indicate that deep learning is full of potential to enhance customer experience through accurate daily dish recognition.

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“…Bolanos and Radeva used GoogleNet to develop a method for simultaneous food localization and recognition. Park et al [6] proposed a new CNN architecture for Korean food image detection and recognition for mobile dietary management. Using VGG network, Yadav and Chand [7] developed another automated food image classification method.…”

Section: Review Of Related Methodsmentioning

confidence: 99%