资源论文Memory Efficient Max Flow for Multi-label Submodular MRFs

Memory Efficient Max Flow for Multi-label Submodular MRFs

2019-12-20 | |  80 |   36 |   0

Abstract

Multi-label submodular Markov Random Fields (MRFs) have been shown to be solvable using max-flflow based on an encoding of the labels proposed by Ishikawa, in which each variable Xi is represented by nodes (where is the number of labels) arranged in a column. However, this method in general requires 2 2 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-flflow 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

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