Overview of graph cuts. Outline. Introduction S-t Graph cuts Extension to multi-label problems Compare simulated annealing and alpha-expansion algorithm. Introduction. Discrete energy minimization methods that can be applied to Markov Random Fields (MRF) with binary labels or multi-labels.
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Max-flow/Min-cut method ：Augmenting paths (Ford Fulkerson Algorithm)
the cost of a cut :
Minimum cut : a cut whose cost is the least over all cutsS-t Graph Cut
E(f) can be minimized by s-t graph cuts
Basic idea : break multi-way cut computation into a sequence of binary s-t cuts.
Each label competes with the other labels for space in the image
Define a move which allows to change pixels from alpha to beta and beta to alpha
Prove in : efficient graph-based energy minimization methods in computer vision
Handles more general energy function
Prove in: what energy functions can be minimized via graph cuts?
Single alpha-expansion move
Large number of pixels can change their labels simultaneously
Only one pixel change its label at a time
Computationally intensive O(2^n)