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Combining deep residual network features with supervised machine learning algorithms to classify diverse food image datasets

McAllister, Patrick, Zheng, Huiru, Bond, RR and Moorhead, Anne (2018) Combining deep residual network features with supervised machine learning algorithms to classify diverse food image datasets. Computers in Biology and Medicine, 1 . [Journal article]

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URL: http://dx.doi.org/10.1016/j.compbiomed.2018.02.008

DOI: 10.1016/j.compbiomed.2018.02.008


Obesity is increasing worldwide and can cause many chronic conditions such as type-2 diabetes, heart disease, sleep apnea, and some cancers. Monitoring dietary intake through food logging is a key method to maintain a healthy lifestyle to prevent and manage obesity. Computer vision methods have been applied to food logging to automate image classification for monitoring dietary intake. In this work we applied pretrained ResNet-152 and GoogleNet convolutional neural networks (CNNs) to extract features from food image datasets; Food 5K, Food-11, RawFooT-DB, and Food-101. Deep features were extracted from CNNs and used to train machine learning classifiers including artificial neural network(ANN), support vector machine(SVM), Random Forest, fully connected Neural Networks, and Naive Bayes. Results show that using ResNet-152 deep features with SVM with RBF kernel can accurately detect food items with 99.4% accuracy using Food-5K food image dataset. Trained with ResNet-152 features, ANN can achieve 91.34%, 99.28% when applied to Food-11 and RawFooT-DB food image datasets respectively and SVM with RBF kernel can achieve 64.98% with Food-101 image dataset. From this research it is clear that using deep CNN features can be used efficiently for diverse food item image classification. The work presented in this research shows that pretrained ResNet-152 features provide sufficient generalisation power when applied to a range of food image classification tasks.

Item Type:Journal article
Keywords:Obesity; Food logging; Deep learning; Convolutional neural networks; Feature extraction
Faculties and Schools:Faculty of Social Sciences > School of Communication
Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Faculty of Social Sciences
Research Institutes and Groups:Institute of Nursing and Health Research
Institute of Nursing and Health Research > Managing Chronic Illness Research Centre
Computer Science Research Institute > Smart Environments
Computer Science Research Institute
ID Code:39619
Deposited By: Dr Huiru Zheng
Deposited On:05 Mar 2018 15:41
Last Modified:18 Feb 2019 23:23

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