Fast approximate energy minimization via graph cuts

Fast Approximate Energy Minimization via Graph Cuts

In this paper we address the problem of minimizing a large class of energy functions that occur in early vision. The major restriction is that the energy function’s smoothness term must only involve pairs of pixels. We propose two algorithms that use graph cuts to compute a local minimum even when very large moves are allowed. The first move we consider is an α-βswap: for a pair of labels α, β,...

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Generalized Fast Approximate Energy Minimization via Graph Cuts: Alpha-Expansion Beta-Shrink Moves

We present α-expansion β-shrink moves, a simple generalization of the widely-used αβswap and α-expansion algorithms for approximate energy minimization. We show that in a certain sense, these moves dominate both αβ-swap and α-expansion moves, but unlike previous generalizations the new moves require no additional assumptions and are still solvable in polynomial-time. We show promising experimen...

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Generalized Fast Approximate Energy Minimization via Graph Cuts: a-Expansion b-Shrink Moves

We present α-expansion β-shrink moves, a simple generalization of the widely-used αβswap and α-expansion algorithms for approximate energy minimization. We show that in a certain sense, these moves dominate both αβ-swap and α-expansion moves, but unlike previous generalizations the new moves require no additional assumptions and are still solvable in polynomial-time. We show promising experimen...

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Generalized Fast Approximate Energy Minimization via Graph Cuts: α-Expansion β-Shrink Moves

We present α-expansion β-shrink moves, a simple generalization of the widely-used αβswap and α-expansion algorithms for approximate energy minimization. We show that in a certain sense, these moves dominate both αβ-swap and α-expansion moves, but unlike previous generalizations the new moves require no additional assumptions and are still solvable in polynomial-time. We show promising experimen...

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Image Segmentation by Graph Cuts via Energy Minimization

Multiregion graph cut image partitioning via kernel mapping is used to segment any type of the image data. The image data is transformed by a kernel function so that the piecewise constant model of the graph cut formulation becomes applicable. The objective function contains an original data term to evaluate the deviation of the transformed data within each segmentation region, from the piecewi...

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Fast approximate energy minimization via graph cuts

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