In the traditional image matching algorithms based on spectral features,the Euclidean distance metric can not fairly reflect the underlying relationship between the dimensions of sample data,and large deformation and outliers will lead to poor matching accuracy and stability.In order to solve these problems existing in the structure of spectral features,an image matching algorithm based on Mahalanobis-distance spectral features is proposed.In the algorithm,first,a local undirected weighted graph is constructed in sub feature point sets by using the Maha- lanobis distance.Next,the singular value decomposition of the adjacent matrixes of the graph is performed,and the Mahalanobis-distance spectral features describing the attributes of the point sets are constructed by using spec- tral value vectors.Then,a matching matrix is constructed based on the Mahalanobis-distance spectral features,and the matching relationships among the feature points of the image are obtained by using the greedy algorithm.Finally,in order to further improve the matching accuracy,the false matching points are eliminated by means of the SVM method.A large number of experimental results show that the proposed algorithm improves the matching accuracy,and it is robust to outliers.