FoodAI

Sahoo, Debasish; Wang, Hao; Ke, Shu; Wu, Xiongwei; Lê, Hung; Achananuparp, Palakorn; Lim, Ee-Peng; Hoi, Steven C. H.

doi:10.1145/3292500.3330734

Cited by 63 publications

(17 citation statements)

References 21 publications

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“…There are several existing works in the literature that apply transfer learning to train CNN-based food classification models [15], [18], [19], [20], [21]. Meyers et al [19] developed Im2Calories, a food diary application that uses an ensemble of CNN classifiers for food detection, classification, segmentation, and volume estimation.…”

Section: A Food Recognition and Classificationmentioning

confidence: 99%

“…The author's goal is to classify fresh food ingredient images indexed in the Mealcome image dataset [22]. In the most recent example, Sahoo et al [21] developed FoodAI, a CNN model trained to classify various dishes with a focus on food found locally in Singapore. The model is able to predict a wide variety of Singaporean and South-East Asian dishes, recognizing 756 distinct food classes.…”

Section: A Food Recognition and Classificationmentioning

confidence: 99%

“…The model's performance has been shown to improve through manual preprocessing of the apple pie class, indicating that there is a possible performance gain if all the images were manually processed. In particular, FoodAI [21] managed to achieve a top-1 accuracy of 83.2% where the authors put significant effort into manual inspection of the food images by domain experts while constructing the dataset. However, in our case, time and resource constraints limit the manual preprocessing of the dataset.…”

Section: Limitationsmentioning

confidence: 99%

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Mobile Food Journalling Application with Convolutional Neural Network and Transfer Learning: A Case for Diabetes Management in Malaysia

Chew¹,

Sebastian²,

Raman³

et al. 2022

IJACSA

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Diabetes is an ever worsening problem in modern society, placing a heavy burden on healthcare systems. Due to the association between obesity and diabetes, food journaling mobile applications are an effective approach for managing and improving the outcome of diabetics. Due to the efficacy of nutritional tracking and management in managing diabetes, we implemented a deep learning-based Convolutional Neural Network food classification model to aid with food logging. The model is trained on a subset of the Food-101 and Malaysian Food 11 datasets, including web-scraped images, with a focus on food items found locally in Malaysia. In our experiments, we explore how fine-tuning of the image dataset improves the performance of the model.

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Section: A Food Recognition and Classificationmentioning

confidence: 99%

Section: A Food Recognition and Classificationmentioning

confidence: 99%

Section: Limitationsmentioning

confidence: 99%

See 1 more Smart Citation

Mobile Food Journalling Application with Convolutional Neural Network and Transfer Learning: A Case for Diabetes Management in Malaysia

Chew¹,

Sebastian²,

Raman³

et al. 2022

IJACSA

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“…Convolutional neural networks have achieved state-of-the-art performances on many food recognition datasets [17][18][19][20][21]. Recent studies have explored a variety of CNN-based methods for their datasets.…”

Section: Related Workmentioning

confidence: 99%

Progressive Kernel Extreme Learning Machine for Food Image Analysis via Optimal Features from Quality Resilient CNN

Tahir

Loo

2021

Applied Sciences

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Recently, food recognition has received more research attention for mHealth applications that use automated visual-based methods to assess dietary intake. The goal is to improve the food diaries by addressing the challenges faced by existing methodologies. In addition to the classical challenge of the absence of rigid food structure and intra-class variations, food diaries employing deep networks trained with pristine images are susceptible to quality variations in real-world conditions of image acquisition and transmission. Similarly, existing progressive classifiers that use visual features via a convolutional neural network (CNN) classify food categories and cannot detect food ingredients. We aim to provide a system that selects the optimal subset of features from quality resilient CNNs and subsequently incorporates the parallel type of classification to tackle such challenges. The first progressive classifier recognizes food categories, and its multilabel extension detects food ingredients. Following this idea, after extracting features from the quality resilient category and ingredient CNN models by fine-tuning it on synthetic images generated using the novel online data augmentation method random iterative mixup. Our feature selection strategy uses the Shapley additive explanation (SHAP) values from the gradient explainer to select the best features. Then, novel progressive kernel extreme learning machine (PKELM) is exploited to cater to domain variations due to quality distortions, intra-class variations, and so forth, by remodeling the network structure based on activity value with the nodes. PKELM extension for multilabel classification detects ingredients by employing a bipolar step function to process test output and then selecting the column labels of the resulting matrix with a value of one. Moreover, during online learning, the PKELM novelty detection mechanism can label unlabeled instances and detect noisy samples. Experimental results showed superior performance on an integrated set of measures for seven publicly available food datasets.

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“… 13 One of the real-world examples would be FoodAI, a deep learning-based image recognition mobile food journal. 17 It has been deployed through several organisations and universities, allowing for an efficient and effective diet monitoring tool to overcome the aforementioned limitations.…”

Section: Introductionmentioning

confidence: 99%

MyDietCam: Development and usability study of a food recognition integrated dietary monitoring smartphone application

et al. 2023

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Background Diet monitoring has been linked with improved eating habits and positive health outcomes such as prevention of obesity. However, this is often unsustainable as traditional methods place a high burden on both participants and researchers through pen and paper recordings and manual nutrient coding respectively. The digitisation of dietary monitoring has greatly reduced these barriers. This paper proposes a diet application with a novel food recognition feature with a usability study conducted in the real world. Methods This study describes the development of a mobile diet application (MyDietCam) targeted at healthy Malaysian adults. Focus group discussions (FGD) were carried out among dietitians and potential users to determine ideal features in a diet application. Thirty participants were recruited from a local university to log their meals through MyDietCam for six days and submit the Malay mHealth Application Usability Questionnaire (M-MAUQ) at the end of the study. Results The findings from the FGD led to the implementation of the main features: individualised recommendations, food logging through food recognition to reduce steps for data entry and provide detailed nutrient analyses through visuals. An average overall usability score of 5.13 out of a maximum of seven was reported from the M-MAUQ which is considered acceptable. Conclusion The development of a local (Malaysian) mobile diet application with acceptable usability may be helpful in sustaining the diet monitoring habit to improve health outcomes. Future work should focus on improving the issues raised before testing the effectiveness of the application for improving health outcomes.

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FoodAI

Cited by 63 publications

References 21 publications

Mobile Food Journalling Application with Convolutional Neural Network and Transfer Learning: A Case for Diabetes Management in Malaysia

Mobile Food Journalling Application with Convolutional Neural Network and Transfer Learning: A Case for Diabetes Management in Malaysia

Progressive Kernel Extreme Learning Machine for Food Image Analysis via Optimal Features from Quality Resilient CNN

MyDietCam: Development and usability study of a food recognition integrated dietary monitoring smartphone application

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