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Solving Multilabel Graph Cut Problems with Multilabel Swap

Peter Carr, Richard Hartley
2009 2009 Digital Image Computing: Techniques and Applications  
can be solved using multilabel graph cuts.  ...  Approximate solutions to labelling problems can be found using binary graph cuts and either the α-expansion or α − β swap algorithms.  ...  MULTILABEL SWAP The multilabel swap algorithm is based on regular multilabel graph cuts.  ... 
doi:10.1109/dicta.2009.90 dblp:conf/dicta/CarrH09b fatcat:kqvqkon26fcpvolqql5pkjcvtm

A graph-cut based algorithm for approximate MRF optimization

Aymen Shabou, Florence Tupin, Jerome Darbon
2009 2009 16th IEEE International Conference on Image Processing (ICIP)  
This paper copes with the approximate minimization of Markovian energy with pairwise interactions. We extend previous approaches that rely on graph-cuts and move making techniques.  ...  We present here a graph construction such that its s-t minimum-cut yields an optimal multilabel move.  ...  On the left, a part of the graph G m defined on three pixels. A cut is depicted and arcs are in the cut are dotted whereas continuous ones are not. A part of the graph is highlighted on the right.  ... 
doi:10.1109/icip.2009.5414189 dblp:conf/icip/ShabouTD09 fatcat:3bkbrddrhfeivc3ag2w2y3bjma

Space-Varying Color Distributions for Interactive Multiregion Segmentation: Discrete versus Continuous Approaches [chapter]

Claudia Nieuwenhuis, Eno Töppe, Daniel Cremers
2011 Lecture Notes in Computer Science  
The commonly used alpha expansion for solving general multilabel MRFs is based on iteratively solving binary problems.  ...  The alpha expansion algorithm iterates over all possible labels α and each time solves a binary graph cut problem. This process is repeated until convergence.  ... 
doi:10.1007/978-3-642-23094-3_13 fatcat:h3xbmm5t3fhsnark3gp5uer7f4

Structural annotation of em images by graph cut

Hang Chang, Manfred Auer, Bahram Parvin
2009 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro  
Next, a graph is constructed with weighted edges on the energy function and is optimized with the graph cut algorithm.  ...  As a result, the method combines the advantages of the level set method and graph cut algorithm, i.e., "topological" invariance and computational efficiency.  ...  Based on the number of terminals, the graph cut could be classified into two groups: classical two-label graph cut and multilabel graph cut.  ... 
doi:10.1109/isbi.2009.5193249 dblp:conf/isbi/ChangAP09 fatcat:kn7ts3rnkfhxvexiyj6staubzy

Solving Multilabel MRFs Using Incremental α-Expansion on the GPUs [chapter]

Vibhav Vineet, P. J. Narayanan
2010 Lecture Notes in Computer Science  
We improve the basic push-relabel implementation of graph cuts using the atomic operations of the GPU and by processing blocks stochastically.  ...  We also reuse the flow using reparametrization of the graph from cycle to cycle and iteration to iteration for fast performance. We show results on various vision problems on standard datasets.  ...  Kohli and Torr [4] describe a reparameterization of the graph to initialize it for later frames in dynamic graph cuts. Komodakis et al. [2] extends this concept to multilabeling problems.  ... 
doi:10.1007/978-3-642-12297-2_61 fatcat:jaen6d4e2feohkcee4sli7ryfu

A Primal-Dual Algorithm for Higher-Order Multilabel Markov Random Fields

Alexander Fix, Chen Wang, Ramin Zabih
2014 2014 IEEE Conference on Computer Vision and Pattern Recognition  
Graph cuts method such as α-expansion [4] and fusion moves [22] have been successful at solving many optimization problems in computer vision.  ...  In this paper we propose a new primal-dual energy minimization method for arbitrary higher-order multilabel MRF's.  ...  Graph Cut Methods and Higher-Order MRF's The most popular graph cut methods for multilabel firstorder MRF's rely on move-making techniques.  ... 
doi:10.1109/cvpr.2014.149 dblp:conf/cvpr/FixWZ14 fatcat:jbbiuqqnebhclin5fm3zmmultu

Multi-label Moves for MRFs with Truncated Convex Priors [chapter]

Olga Veksler
2009 Lecture Notes in Computer Science  
Optimization with graph cuts became very popular in recent years. While exact optimization is possible in a few cases, many useful energy functions are NP hard to optimize.  ...  Two move-making algorithms based on graph cuts are in wide use, namely the swap and expansion. Both of these moves are binary in nature, that is they give each pixel a choice of only two labels.  ...  Energy Optimization with Graph Cuts In this section, we briefly explain the relevant prior work on optimization with graph cuts.  ... 
doi:10.1007/978-3-642-03641-5_1 fatcat:mgc7ni5gsbalxd3vqkq3nsyj3y

Multi-label Moves for MRFs with Truncated Convex Priors

Olga Veksler
2011 International Journal of Computer Vision  
Optimization with graph cuts became very popular in recent years. While exact optimization is possible in a few cases, many useful energy functions are NP hard to optimize.  ...  Two move-making algorithms based on graph cuts are in wide use, namely the swap and expansion. Both of these moves are binary in nature, that is they give each pixel a choice of only two labels.  ...  Energy Optimization with Graph Cuts In this section, we briefly explain the relevant prior work on optimization with graph cuts.  ... 
doi:10.1007/s11263-011-0491-6 fatcat:e3va2t7jezetxm67zxvqe73gd4

Iteratively reweighted graph cut for multi-label MRFs with non-convex priors

Thalaiyasingam Ajanthan, Richard Hartley, Mathieu Salzmann, Hongdong Li
2015 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
In particular, we consider the scenario where the global minimizer of the weighted surrogate energy can be obtained by a multi-label graph cut algorithm, and show that our algorithm then lets us handle  ...  We demonstrate the benefits of our method over state-of-the-art MRF energy minimization techniques on stereo and inpainting problems.  ...  The first class of such methods consists of move-making techniques that were inspired by the success of the graph cut algorithm at solving binary problems in computer vision.  ... 
doi:10.1109/cvpr.2015.7299150 dblp:conf/cvpr/AjanthanHSL15 fatcat:5uj7plyw2rgudbiimllcbwhudu

Markov Models for Image Labeling

S. Y. Chen, Hanyang Tong, Carlo Cattani
2012 Mathematical Problems in Engineering  
Two distinct kinds of discrete optimization methods, that is, belief propagation and graph cut, are discussed.  ...  One of the most successful applications is to solve image labeling problems in computer vision. This paper provides a survey of recent advances in this field.  ...  Graph Cut Graph cut GC was first applied in computer vision by Greig et al. 40 , which describes a large family of MRF inference algorithms based on solving min-cut/max-flow problems.  ... 
doi:10.1155/2012/814356 fatcat:ssio7x76yzapva3vzzkh2sa4gu

Minimizing energy functions on 4-connected lattices using elimination

Peter Carr, Richard Hartley
2009 2009 IEEE 12th International Conference on Computer Vision  
Such functions are often minimized using graph-cuts/max-flow, but this method is only applicable to submodular problems.  ...  We compare the algorithm's performance against alternative methods for solving non-submodular problems, with favourable results.  ...  We would like to thank Fangfang Lu for her assistance with the Elimination algorithm.  ... 
doi:10.1109/iccv.2009.5459450 dblp:conf/iccv/CarrH09 fatcat:nkmclrzybrgozbftlzggw6wzum

Spine Image Fusion Via Graph Cuts

B. Miles, I. B. Ayed, M. W. K. Law, G. Garvin, A. Fenster, Shuo Li
2013 IEEE Transactions on Biomedical Engineering  
This study investigates a novel CT/MR spine image fusion algorithm based on graph cuts.  ...  We state the problem as a discrete multilabel optimization of an energy functional that balances the contributions of three competing terms: (1) a squared error, which encourages the solution to be similar  ...  ACKNOWLEDGMENT The authors would like to thank the clinical collaborators who have discussed these ideas with us.  ... 
doi:10.1109/tbme.2013.2243448 pmid:23372071 fatcat:pgvfnoittfcvthzkcf6ysf3qeu

Conditional Random Fields for Image Labeling

Tong Liu, Xiutian Huang, Jianshe Ma
2016 Mathematical Problems in Engineering  
This paper reviews the research development and status of object recognition with CRFs and especially introduces two main discrete optimization methods for image labeling with CRFs: graph cut and mean  ...  of the label bias problem with respect to MEMMs (maximum entropy Markov models) and HMMs (hidden Markov models).  ...  recent years. [59] first applied the graph cut in computer vision which describes a large family of MRF inference algorithms based on solving min-cui/maxflow problem.  ... 
doi:10.1155/2016/3846125 fatcat:oq4pelbyz5c5ris7o7xl22rvva

Fast Postprocessing for Difficult Discrete Energy Minimization Problems

Ijaz Akhter, Loong Fah Cheong, Richard Hartley
2020 2020 IEEE Winter Conference on Applications of Computer Vision (WACV)  
Our core contribution is a mapping between the binary min-cut problem and finding the shortest path in a directed acyclic graph.  ...  Using this mapping, we present an algorithm to find an approximate solution for the min-cut problem.  ...  Graph-cut to Shortest Path in DAG Mapping To define the undirected binary cut problem, we consider a graph G consisting of V verticies with edge weights given by a symmetric matrix W.  ... 
doi:10.1109/wacv45572.2020.9093369 dblp:conf/wacv/AkhterCH20 fatcat:qfhmm34vnbehnecm7hzixeehuq

Combinatorial Persistency Criteria for Multicut and Max-Cut

Jan-Hendrik Lange, Bjoern Andres, Paul Swoboda
2019 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
The more advanced ones rely on fast algorithms for upper and lower bounds for the respective cut problems and max-flow techniques for auxiliary min-cut problems.  ...  We propose persistency criteria for the multicut and max-cut problem as well as fast combinatorial routines to verify them.  ...  For solving the max-flow problems that occur in our method, we use Boykov-Kolmogorov's algorithm with reused search trees [8, 25] .  ... 
doi:10.1109/cvpr.2019.00625 dblp:conf/cvpr/LangeAS19 fatcat:hysiqvg6rvasvf6q7vng7ezp5y
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