Structured Regression Gradient Boosting

Ferran Diego; Fred A. Hamprecht

CVPR 2016poster0 citations

Structured Regression Gradient Boosting

Abstract

We propose a new way to train a structured output prediction model. More specifically, we train nonlinear data terms in a Gaussian Conditional Random Field (GCRF) by a generalized version of gradient boosting. The approach is evaluated on three challenging regression benchmarks: vessel detection, single image depth estimation and image inpainting. These experiments suggest that the proposed boosting framework matches or exceeds the state-of-the-art.

BibTeX

@inproceedings{cvpr2016_structuredregres,
  title = {Structured Regression Gradient Boosting},
  author = {Ferran Diego and Fred A. Hamprecht},
  booktitle = {CVPR 2016},
  year = {2016}
}