Publication: Deep feature extraction technique based on Conv1D and LSTM network for food image recognition
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2021
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en
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2539-6218
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item.page.harrt.identifier.callno
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Engineering and Applied Science Research
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48
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5
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581
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592
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Copyright (c) 2021 Engineering and Applied Science Research
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Deep feature extraction technique based on Conv1D and LSTM network for food image recognition
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Abstract
There is a global increase in health awareness. The awareness of changing eating habits and choosing foods wisely are key factors that make for a healthy life. In order to design a food image recognition system; many food images were captured from a mobile device but sometimes include non-food objects such as people; cutlery; and even food decoration styles; called noise food images. These issues decreased the performance of the system. Convolutional neural network (CNN) architectures are proposed to address this issue and obtain good performance. In this study; we proposed to use the ResNet50-LSTM network to improve the efficiency of the food image recognition system. The state-of-the-art ResNet architecture was invented to extract the robust features from food images and was employed as the input data for the Conv1D combined with a long short-term memory (LSTM) network called Conv1D-LSTM. Then; the output of the LSTM was assigned to the global average pooling layer before passing to the softmax function to create a probability distribution. While training the CNN model; mixed data augmentation techniques were applied and increased by 0.6%. The results showed that the ResNet50+Conv1D-LSTM network outperformed the previous works on the Food-101 dataset. The best performance of the ResNet50+Conv1D-LSTM network achieved an accuracy of 90.87%.