Abstract: In order to reduce the redundancy of high-dimensional data in the original image space and solve the problem that the distribution of fine-grained data in the feature space cannot be linearly separable, this paper proposes a novel fine-grained image classification method combining low-dimensional visual embedding and non-linear mapping. Firstly, the visual features are embedded into low-dimensional space to reduce the inference caused by the redundant data on the classification and improve the generalization ability of the classification model on the test data. Then, a number of linear classifiers are trained with a ranking based objective function, and the non-linear relationship between the class label and low-dimensional visual embedding is established to effectively distinguish the nuances between the samples of different fine-grained classes. The experimental results demonstrate that, the proposed method improves the classification accuracy on unknown samples and performs better than the existing fine-grained image classification algorithms under the zero-shot setting.