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In this paper, we propose a novel metric learning method based on regularized moving least squares. Unlike most previous metric learning methods which learn a global Mahalanobis distance, we define locally smooth metrics using local affine transformations which are more flexible. The data set after metric learning can preserve the original topological structures. Moreover, our method is fairly efficient and may be used as a preprocessing step for various subsequent learning tasks, including classification, clustering, and nonlinear dimensionality reduction. In particular, we demonstrate that our method can boost the performance of content-based image retrieval (CBIR) tasks. Experimental results provide empirical evidence for the effectiveness of our approach.