Abstract : We propose working-set/greedy algorithms to efficiently find the solutions to convex optimization problems penalized respectively by the total variation and the Mumford Shah boundary size. Our algorithms exploit the piecewise constant structure of the level-sets of the solutions by recursively splitting them using graph cuts. We obtain significant speed up on images that can be approximated with few level-sets compared to state-of-the-art algorithms .
https://hal.archives-ouvertes.fr/hal-01306786
Contributor : Loic Landrieu
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Submitted on : Monday, April 25, 2016 - 3:17:40 PM
Last modification on : Thursday, February 7, 2019 - 4:56:32 PM
Document(s) archivé(s) le : Tuesday, November 15, 2016 - 11:45:10 AM
Loic Landrieu, Guillaume Obozinski. Cut Pursuit: fast algorithms to learn piecewise constant functions. 19th International Conference on Artificial Intelligence and Statistics (AISTATS 2016) , May 2016, Cadix, Spain. ⟨hal-01306786⟩
