Determining dense semantic correspondences across objects and scenes is a
difficult problem that underpins many higher-level computer vision algorithms.
Unlike canonical dense correspondence problems which consider images that are
spatially or temporally adjacent, semantic correspondence is characterized by
images that share similar high-level structures whose exact appearance and
geometry may differ.
Motivated by object recognition literature and recent work on rapidly
estimating linear classifiers, we treat semantic correspondence as a
constrained detection problem, where an exemplar LDA classifier is learned for
each pixel. Read More