  The matching function for the problem of stereo reconstruction or optical
flow has been traditionally designed as a function of the distance between the
features describing matched pixels. This approach works under assumption, that
the appearance of pixels in two stereo cameras or in two consecutive video
frames does not change dramatically. However, this might not be the case, if we
try to match pixels over a large interval of time.
  In this paper we propose a method, which learns the matching function, that
automatically finds the space of allowed changes in visual appearance, such as
due to the motion blur, chromatic distortions, different colour calibration or
seasonal changes. Furthermore, it automatically learns the importance of
matching scores of contextual features at different relative locations and
scales. Proposed classifier gives reliable estimations of pixel disparities
already without any form of regularization.
  We evaluated our method on two standard problems - stereo matching on KITTI
outdoor dataset, optical flow on Sintel data set, and on newly introduced
TimeLapse change detection dataset. Our algorithm obtained very promising
results comparable to the state-of-the-art.
