Memory Efficient Max Flow for Multi-Label Submodular MRFs.
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- Record sourced from PubMed, PMID 29993772.
- Also identified by DOI 10.1109/TPAMI.2018.2819675.
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Abstract
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 X<sub>i</sub> is represented by l nodes (where l is the number of labels) arranged in a column. However, this method in general requires 2 l<sup>2</sup> 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.