MSFF: Multi-Scale feature fusion for fine-grained image classification

Abstract

Fine-grained image classification is a sub-category classification problem with a common superior category. Aiming at the characteristics of large intra-class differences and small inter-class differences in fine-grained images, this paper proposes a fine-grained image classification method based on multi-scale feature fusion. The method constructs a three-branch network model. The attention module and local extraction module are used to obtain the image of the target object and the image of the parts with strong distinguishing detail features. The depth metric learning is used to shorten the distance from the same data by using misclassification information to improve the classification accuracy; secondly, without using the image bounding box/partial annotation information, the image information of different scales is fused through a parallel network structure; finally, the entire network is optimized by combining the loss functions of the three-branch networks. This method performs end-to-end training collaboratively in a multi-branch network to enhance the ability to express information, thereby improving the accuracy of image classification. To evaluate the effectiveness of our method, fine-grained classification experiments were conducted on three datasets. The experimental results show that the algorithm has higher classification accuracy than other fine-grained classification algorithms.