An exploration of deep transfer learning for food image classification

Image classification is an important problem in computer vision research and is useful in applications such as content-based image retrieval and automated detection systems. In recent years, extensive research has been conducted in this field to classify different types of images. In this paper, we investigate one such domain, namely, food image classification. Classification of food images is useful in applications such as waiter-less restaurants and dietary intake calculators. To this end, we explore the use of pre-trained deep convolutional neural networks (DCNNs) in two ways. First, we use transfer learning and re-train the DCNNs on food images. Second, we extract features from pre-trained DCNNs to train conventional classifiers. We also introduce a new food image database based on Australian dietary guidelines. We compare the performance of these methods on existing databases and the one introduced here. We show that similar levels of accuracy are obtained in both methods, but the training time for the latter is significantly lower. We also perform a comparison with existing methods and show that the methods explored here are comparably accurate to existing methods.