A deformable local image descriptor

This paper presents a novel local image descriptor that is robust to general image deformations. A limitation with traditional image descriptors is that they use a single support region for each interest point. For general image deformations, the amount of deformation for each location varies and is unpredictable such that it is difficult to choose the best scale of the support region. To overcome this difficulty, we propose to use multiple support regions of different sizes surrounding an interest point. A feature vector is computed for each support region, and the concatenation of these feature vectors forms the descriptor for this interest point. Furthermore, we propose a new similarity measure model, local-to-global similarity (LGS) model, for point matching that takes advantage of the multi-size support regions. Each support region acts as a dasiaweakpsila classifier and the weights of these classifiers are learned in an unsupervised manner. The proposed approach is evaluated on a number of images with real and synthetic deformations. The experiment results show that our method outperforms existing techniques under different deformations.