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Locally Smooth metric learning with application to image retrieval

Hong Chang, Dit-Yan Yeung
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 transformation 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.
ICCV 2007, 11th IEEE International conference on computer vision, Rio de Janeiro, Brazil, October 14-20, 2007.