Feature Extraction Using Deep Learning for Food Type Recognition

Abstract

With the widespread use of smartphones, people are taking more and more images of their foods. These images can be used for automatic recognition of foods present and potentially providing an indication of eating habits. Traditional methods rely on computing a number of user derived features from image and then use a classification method to classify food images into different food categories. Pertained deep neural network architectures can be used for automatically extracting features from images for different classification tasks. This work proposes the use of convolutional neural networks (CNN) for feature extraction from food images. A linear support vector machine classifier was trained using 3-fold cross-validation scheme on a publically available Pittsburgh fast-food image dataset. Features from 3 different fully connected layers of CNN were used for classification. Two classification tasks were defined. The first task was to classify images into 61 categories and the second task was to classify images into 7 categories. Best results were obtained using 4096 features with an accuracy of 70.13% and 94.01% for 61 class and 7 class tasks respectively. This shows improvement over previously reported results on the same dataset.