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Showing 1 to 2 of 2 for “"Multi-label Submodularity"”.

  1. Optimization of Markov Random Fields in Computer Vision

    … a memory efficient max-flow algorithm for multi-label submodular MRFs. In fact, such MRFs have been shown to be optimally solvable using max-flow based on an encoding of the labels proposed by Ishikawa, in which each variable $X_i$ is represented by $\ell$ nodes (where $\ell$ is the number …

    aus-cath Repository record for Optimization of Markov Random Fields in Computer Vision (opens in a new tab)

  2. Optimization of Markov Random Fields in Computer Vision

    … a memory efficient max-flow algorithm for multi-label submodular MRFs. In fact, such MRFs have been shown to be optimally solvable using max-flow based on an encoding of the labels proposed by Ishikawa, in which each variable $X_i$ is represented by $\ell$ nodes (where $\ell$ is the number …

    anu Repository record for Optimization of Markov Random Fields in Computer Vision (opens in a new tab)