Method for Recognition of Food Images Based on Improved Attention Model

With the increasing demands for healthy diet of people, various kinds of food evaluation assistant softwares emerge as times require, and the topic of food images recognition receives more and more attention. Food images recognition belongs to fine-grained recognition problem, which is more difficult than other image recognition. Moreover, popular food image datasets, such as ISIA Food-500, ETH Food-101 and Vireo Food-172, contain a small number of images, which makes it difficult to train the image recognition system well and further increasing the recognition difficulty. In this paper, an image recognition method based on attention mechanism is proposed. The method introduces the concept of local-attention on the basis of self-attention to describe the fine-grained features of the image and improve the accuracy of image recognition. In addition, an image self-supervised pre-training algorithm is proposed as well, to alleviate the problem of insufficient training samples of food images. The experimental results show that Top-1 accuracy and Top-5 accuracy of the proposed method on ISIA Food-500 dataset are 65.58% and 90.03%, respectively, which is superior to the state-of-the-art algorithms. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.