Abstract: The content based image retrieval method extracts the color,textural,shape features of images,which can be represented in the feature space,with similarities among them obtained by some distance between feature vectors.Its accuracy critically depends on the feature vectors．However,images in same class will have different features.This paper presented an image retrieval method based on sparse low-rank representation.After the low-rank components of each set was recovered,both the global mixture of subspaces structure and the locally linear structure of the features were captured.The experimental results show that the method not only has a strong robustness to the unstablefeatures,but also has a good retrieval performance.

Key words: Content based imageretrieval,Sparse low-rank representation,Feature extraction