Learning feature distance measures for image correspondences

  title={Learning feature distance measures for image correspondences},
  author={Xi Chen and Tat-Jen Cham},
  journal={2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)},
  pages={560-567 vol. 2}
Standard but ad hoc measures such as sum-of-squared pixel differences (SSD) are often used when comparing and registering two images that have not been previously observed before. In this paper, we propose a framework to address the problem of learning a parametric feature distance measure to measure the dissimilarity between pairs of images. The method is based on optimizing the parameters of the distance measure in order to minimize correspondence classification errors on training data… CONTINUE READING