AISTATS 2016poster21 citations
Cut Pursuit: Fast Algorithms to Learn Piecewise Constant Functions
Loic Landrieu, Guillaume Obozinski
Abstract
We propose working-set/greedy algorithms to efficiently solve problems penalized respectively by the total variation and the Mumford Shah boundary size when the piecewise constant solutions has a small number of levelsets. Our algorithms exploit this structure by recursively splitting the level-sets using graph cuts. We obtain significant speed up on images that can be approximated with few levelsets compared to state-of-the-art algorithms.
BibTeX
@InProceedings{pmlr-v51-landrieu16,
title = {Cut Pursuit: Fast Algorithms to Learn Piecewise Constant Functions},
author = {Landrieu, Loic and Obozinski, Guillaume},
booktitle = {Proceedings of the 19th International Conference on Artificial Intelligence and Statistics},
pages = {1384--1393},
year = {2016},
editor = {Gretton, Arthur and Robert, Christian C.},
volume = {51},
series = {Proceedings of Machine Learning Research},
address = {Cadiz, Spain},
month = {09--11 May},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v51/landrieu16.pdf},
url = {https://proceedings.mlr.press/v51/landrieu16.html},
abstract = {We propose working-set/greedy algorithms to efficiently solve problems penalized respectively by the total variation and the Mumford Shah boundary size when the piecewise constant solutions has a small number of levelsets. Our algorithms exploit this structure by recursively splitting the level-sets using graph cuts. We obtain significant speed up on images that can be approximated with few levelsets compared to state-of-the-art algorithms.}
}