Live briefing
Everything about Graph cut optimization
Combinatorial optimization method for a family of functions of discrete variables
Graph cut optimization is a combinatorial optimization method applicable to a family of functions of discrete variables, named after the concept of cut in the theory of flow networks. Thanks to the max-flow min-cut theorem, determining the minimum cut over a graph representing a flow network is equivalent to computing the maximum flow over the network. Given a pseudo-Boolean function, if it is possible to construct a flow network with positive weights such thateach cut of the network can be mapped to an assignment of variables to, and the cost of equals Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Graph cut optimizationA 5-minute orientation
- Skim a recent paper: Large-scale UAV image stitching based on global registration optimization and graph-cut methodInfluential work since 2022
- Pick up a classic book: Discrete and combinatorial mathematicsThe most-reprinted book on the subject
- Read the landmark paper: Space-Time Super-Resolution Using Graph-Cut OptimizationCited 79 times
- Go sideways: Submodular set functionA closely related topic
- Go sideways: Quadratic pseudo-Boolean optimizationA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🏆 Landmark papers (most cited)
Space-Time Super-Resolution Using Graph-Cut Optimization
A Surface Reconstruction Method Using Global Graph Cut Optimization
Graph-Cut RANSAC: Local Optimization on Spatially Coherent Structures
Efficient multilevel image segmentation through fuzzy entropy maximization and graph cut optimization
Bayesian graph-cut optimization for wall surfaces reconstruction in indoor environments
Order-Preserving Moves for Graph-Cut-Based Optimization
Moving object detection based on improved VIBE and graph cut optimization
Fast graph-cut based optimization for practical dense deformable registration of volume images
🆕 Influential since 2022
Large-scale UAV image stitching based on global registration optimization and graph-cut method
On Alpha-Expansion-Based Graph-Cut Optimization for Decoder-Side Depth Estimation
Comparison of hyperplane rounding for max-cut and quantum approximate optimization algorithm over certain regular graph families
A Novel Deep Learning Architecture for Real-Time Autonomous Driving Perception with Fuzzy Graph-Cut and Metaheuristic Optimization
Patch-based Graph Cut Optimization for 3D Line Segment Extraction of Building Structures from Outdoor Point Cloud Data
📚 Books worth reading
Discrete and combinatorial mathematicsRalph P. Grimaldi · 1985
Data structures and algorithm analysis in CMark Allen Weiss · 1993
Discrete mathematicsNorman Biggs · 1985
Data structures & algorithm analysis in JavaMark Allen Weiss · 1999
Fundamentals of data structures in CEllis Horowitz · 1993
Information Processing in Medical ImagingJames C. Gee · 2013
Mathematical programming at Oberwolfach IIB. H. Korte, M. L. Balinski · 1984
Linguistic structure predictionNoah A. Smith · 2011
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Submodular set function In mathematics, a submodular set function (also known as a submodular function) is a set function that, informally, describes the relationship between…
- Quadratic pseudo-Boolean optimization Quadratic pseudo-Boolean optimisation (QPBO) is a combinatorial optimization method for minimizing quadratic pseudo-Boolean functions in the form f ( x…
- Tau function (integrable systems) Tau functions are an important ingredient in the modern mathematical theory of integrable systems, and have numerous applications in a variety of other…
- Phase-type distribution A phase-type distribution is a probability distribution constructed by a convolution or mixture of exponential distributions. It results from a system…
- Tensor derivative (continuum mechanics) The derivatives of scalars, vectors, and second-order tensors with respect to second-order tensors are of considerable use in continuum mechanics. These…
- Compatibility (mechanics) In continuum mechanics, a compatible deformation (or strain) tensor field in a body is that unique tensor field that is obtained when the body is subjected…
- Quantum optimization algorithms Quantum optimization algorithms are quantum algorithms that are used to solve optimization problems. Mathematical optimization deals with finding the best…
- Manifold regularization In machine learning, manifold regularization is a technique for using the shape of a dataset to constrain the functions that should be learned on that…