Fine-grained recognition of Chinese food image based on DenseNet with attention mechanism

Abstract

Recently, deep learning convolutional neural network has been widely used in food recognition and other fields, but there are still some problems such as poor semantic feature extraction ability and low recognition accuracy of Chinese food images. Based on the above reasons, this paper proposed a DenseNet model based on attention mechanism for image recognition of Chinese food. Using the idea of transfer learning, this paper applied the DenseNet pre-training model with excellent classification performance to the Chinese food image dataset. In order to improve the ability of extracting distinguishable features and learning fine-grained features of Chinese food images, this paper proposed an attention mechanism based on the DenseNet-169 model. This paper used the attention module to extract the key semantic feature map of the image, and then carried out the adaptive multi-fine-grained region tailoring. Finally, based on the region weight fusion scheme, the multi-fine-grained region feature map was integrated into an image feature description and sent to DenseNet-169 for recognition, so as to realize the end-to-end image recognition network. Experiments were carried out on VIREO Food-172 dataset, and the results shows that our method achieves considerable and comparable recognition performance to the state-of-art.