Cut Pursuit: fast algorithms to learn piecewise constant functions on general weighted graphs - Archive ouverte HAL Access content directly
Journal Articles SIAM Journal on Imaging Sciences Year : 2017

Cut Pursuit: fast algorithms to learn piecewise constant functions on general weighted graphs

Abstract

We propose working-set/greedy algorithms to efficiently solve problems penalized respectively by the total variation on a general weighted graph and its L0 counterpart the Mumford Shah total level-set boundary size when the piecewise constant solutions have a small number of distinct level-sets; this is typically the case when the total level-set boundary size is small, which is encouraged by these two forms of penalization. Our algorithms exploit this structure by recursively splitting the level-sets of a piecewise-constant candidate solution using graph cuts. We obtain significant speed-ups over state-of-the-art algorithms for images that are well approximated with few level-sets
Fichier principal
Vignette du fichier
cutpursuit.pdf (8 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01306779 , version 1 (26-04-2016)
hal-01306779 , version 2 (18-01-2017)
hal-01306779 , version 3 (31-07-2017)
hal-01306779 , version 4 (21-08-2017)

Licence

Attribution - NonCommercial

Identifiers

  • HAL Id : hal-01306779 , version 4

Cite

Loic Landrieu, Guillaume Obozinski. Cut Pursuit: fast algorithms to learn piecewise constant functions on general weighted graphs. SIAM Journal on Imaging Sciences, 2017, Vol. 10 ( No. 4 ), pp. 1724-1766. ⟨hal-01306779v4⟩
1358 View
1322 Download

Share

Gmail Facebook X LinkedIn More