Abstract: The
We consider the problem of learning to map between two
vector spaces given pairs of matching vectors, one from
each space. This problem naturally arises in numerous vision
problems, for example, when mapping between the images
of two cameras, or when the annotations of each image
is multidimensional. We focus on the common asymmetric
case, where one vector space X is more informative than
the other Y, and find a transformation from Y to X. We
present a new optimization problem that aims to replicate
in the transformed Y the margins that dominate the structure
of X. This optimization problem is convex, and efficient
algorithms are presented. Links to various existing methods
such as CCA and SVM are drawn, and the effectiveness of
the method is demonstrated in several visual domains.
Reference:
Lior Wolf, Nathan
Manor, "Visual Recognition using Mappings that Replicate Margins," Computer Vision Pattern Recognition (CVPR),
Jun. 2010.
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here for a matlab code that solves MRM efficiently
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here for a data-set creating the serveliance example below, run main_test_full_run
Last
update 11th
of Jun, 2010