Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Using Dynamic Programming for Solving Variational Problems in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Graphcut textures: image and video synthesis using graph cuts
ACM SIGGRAPH 2003 Papers
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Interactive digital photomontage
ACM SIGGRAPH 2004 Papers
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Convergent Tree-Reweighted Message Passing for Energy Minimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Minimizing Nonsubmodular Functions with Graph Cuts-A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Solving Multilabel Graph Cut Problems with Multilabel Swap
DICTA '09 Proceedings of the 2009 Digital Image Computing: Techniques and Applications
Fusion Moves for Markov Random Field Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Order-Preserving Moves for Graph-Cut-Based Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
High-accuracy stereo depth maps using structured light
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Fast Approximate Energy Minimization with Label Costs
International Journal of Computer Vision
Making the right moves: Guiding alpha-expansion using local primal-dual gaps
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Exact optimization for Markov random fields with convex priors
IEEE Transactions on Pattern Analysis and Machine Intelligence
A tiered move-making algorithm for general pairwise MRFs
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Computer Vision and Image Understanding
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Expansion algorithm is a popular optimization method for labeling problems. For many common energies, each expansion step can be optimally solved with a min-cut/max flow algorithm. While the observed performance of max-flow for the expansion algorithm is fast, its theoretical time complexity is worse than linear in the number of pixels. Recently, Dynamic Programming (DP) was shown to be useful for 2D labeling problems via a "tiered labeling" algorithm, although the structure of allowed (tiered) is quite restrictive. We show another use of DP in a 2D labeling case. Namely, we use DP for an approximate expansion step. Our expansion-like moves are more limited in the structure than the max-flow expansion moves. In fact, our moves are more restrictive than the tiered labeling structure, but their complexity is linear in the number of pixels, making them extremely efficient in practice. We illustrate the performance of our DP-expansion on the Potts energy, but our algorithm can be used for any pairwise energies. We achieve better efficiency with almost the same energy compared to the max-flow expansion moves.