Abstract: To widen the application scope of metric learning in image classification and improve the performance of classification, an image classification algorithm based on elliptic hyperbolic mahalanobis metric is proposed. Firstly, the algorithm combines the color feature and the texture feature described by local binary patterns (LBPs) to represent the image feature. Then, elliptic hyperbolic metric which has better adaptability to the sample data is introduced and the elliptic hyperbolic mahalanobis metric is defined according to the statistical characteristics of the data, and the elliptic hyperbolic mahalanobis metric learning is presented to obtain the optimal metric matrix. Finally, the samples are transformed into a new feature space by using the elliptic hyperbolic mahalanobis metric matrix to reduce the correlation between each dimension of the feature and complete the classification by calculating the distance between the features of the images. Experiments show that the proposed algorithm improves the effectiveness of image classification.