Calibrating Parameters of Cost Functionals
Abstract
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 extensions to deal with data-dependent energies.
Cite
Text
Younes. "Calibrating Parameters of Cost Functionals." European Conference on Computer Vision, 2000. doi:10.1007/3-540-45053-X_14Markdown
[Younes. "Calibrating Parameters of Cost Functionals." European Conference on Computer Vision, 2000.](https://mlanthology.org/eccv/2000/younes2000eccv-calibrating/) doi:10.1007/3-540-45053-X_14BibTeX
@inproceedings{younes2000eccv-calibrating,
title = {{Calibrating Parameters of Cost Functionals}},
author = {Younes, Laurent},
booktitle = {European Conference on Computer Vision},
year = {2000},
pages = {212-223},
doi = {10.1007/3-540-45053-X_14},
url = {https://mlanthology.org/eccv/2000/younes2000eccv-calibrating/}
}