Robust Matching Method for Scale and Rotation Invariant Local Descriptors and Its Application to Image Indexing

Interest point matching is widely used for image indexing. In this paper we introduce a new distance measure between two local descriptors instead of conventional Mahalanobis distance to improve matching accuracy. From experiments with synthetic images we show that the error distribution of local jet is gaussian but the distribution of the descriptors derived from local jet is not gaussian. Based on the observation, we design a new distance measure between two local descriptors and improve accuracy of point matching. We also reduce the number of candidate points and reduce the computational cost by taking into account the characteristic scale ratio. Experimental results confirm the validity of our method.