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 Xi is represented ℓ by nodes (where ℓ is the number of labels) arranged in a column. However, this method in general requires 2ℓ2 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.

Publications

Memory Efficient Max Flow for Multi-label Submodular MRFs.
Thalaiyasingam Ajanthan, Richard Hartley, and Mathieu Salzmann.
IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), March 2018.

[pdf] [arxiv] [code]

Memory Efficient Max Flow for Multi-label Submodular MRFs.
Thalaiyasingam Ajanthan, Richard Hartley, and Mathieu Salzmann.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016.

[pdf] [supplementary] [poster] [code]