Calibrating parameters of cost functionals in computer vision

M. I. Miller and L. Younes

We propose a new framework for calibrating parameters of energy
functionals, as used in image analysis. The method learns
parameters from a family of correct examples, and given a
probabilistic construct for generating wrong examples from correct
ones. We introduce a measure of frustration to penalize
cases in which wrong responses are preferred to correct ones, and
we design a stochastic gradient algorithm which converges
to parameters which minimize this measure of frustration. We also
present a first set of experiments in this context, and introduce
possible extensions to deal with data-dependent energies.