Memory Efficient Max Flow for Multi-Label Submodular MRFs
Multi-label submodular Markov Random Fields (MRFs) have been shown to be solvable using max-flow based on an encoding of the labels proposed by Ishikawa, in which each variable Xi is represented by l nodes (where l is the number of labels) arranged in a column. However, this method in general requir...
| Publié dans: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 41(2019), 4 vom: 26. Apr., Seite 886-900 |
|---|---|
| Auteur principal: | |
| Autres auteurs: | , |
| Format: | Article en ligne |
| Langue: | English |
| Publié: |
2019
|
| Accès à la collection: | IEEE transactions on pattern analysis and machine intelligence |
| Sujets: | Journal Article |
| Résumé: | Multi-label submodular Markov Random Fields (MRFs) have been shown to be solvable using max-flow based on an encoding of the labels proposed by Ishikawa, in which each variable Xi is represented by l nodes (where l is the number of labels) arranged in a column. However, this method in general requires 2 l2 edges for each pair of neighbouring variables. This makes it inapplicable to realistic problems with many variables and labels, due to excessive memory requirement. In this paper, we introduce a variant of the max-flow algorithm that requires much less storage. Consequently, our algorithm makes it possible to optimally solve multi-label submodular problems involving large numbers of variables and labels on a standard computer |
|---|---|
| Description: | Date Revised 20.11.2019 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 1939-3539 |
| DOI: | 10.1109/TPAMI.2018.2819675 |