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Counting minimum (s, t)cuts in weighted planar graphs in polynomial time
"... Abstract. We give an O(nd+n log n) algorithm computing the number of minimum (s, t)cuts in weighted planar graphs, where n is the number of vertices and d is the length of the shortest st path in the corresponding unweighted graph. Previously, Ball and Provan gave a polynomialtime algorithm for u ..."
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Abstract. We give an O(nd+n log n) algorithm computing the number of minimum (s, t)cuts in weighted planar graphs, where n is the number of vertices and d is the length of the shortest st path in the corresponding unweighted graph. Previously, Ball and Provan gave a polynomialtime algorithm
An Analysis on Minimum st Cut Capacity of Random Graphs with Specified Degree Distribution
"... Abstract—The capacity (or maximum flow) of an unicast network is known to be equal to the minimum st cut capacity due to the maxflow mincut theorem. If the topology of a network (or link capacities) is dynamically changing or unknown, it is not so trivial to predict statistical properties on the ..."
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Abstract—The capacity (or maximum flow) of an unicast network is known to be equal to the minimum st cut capacity due to the maxflow mincut theorem. If the topology of a network (or link capacities) is dynamically changing or unknown, it is not so trivial to predict statistical properties
Counting and sampling minimum (s, t)cuts in weighted planar graphs in polynomial time
"... We give an O(nd + n log n) algorithm computing the number of minimum (s, t)cuts in weighted planar graphs, where n is the number of vertices and d is the length of the shortest st path in the corresponding unweighted graph. Previously, Ball and Provan gave a polynomialtime algorithm for unweighte ..."
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Cited by 4 (2 self)
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We give an O(nd + n log n) algorithm computing the number of minimum (s, t)cuts in weighted planar graphs, where n is the number of vertices and d is the length of the shortest st path in the corresponding unweighted graph. Previously, Ball and Provan gave a polynomialtime algorithm
A competitive study of the pseudoflow algorithm for the minimum s–t cut problem in vision applications
, 2013
"... ..."
Computing minimum cuts by randomized search heuristics
 Collaborative Research Center 531, Technical University of Dortmund
, 2008
"... We study the minimum stcut problem in graphs with costs on the edges in the context of evolutionary algorithms. Minimum cut problems belong to the class of basic network optimization problems that occur as crucial subproblems in many realworld optimization problems and have a variety of applicati ..."
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Cited by 10 (4 self)
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We study the minimum stcut problem in graphs with costs on the edges in the context of evolutionary algorithms. Minimum cut problems belong to the class of basic network optimization problems that occur as crucial subproblems in many realworld optimization problems and have a variety
A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts
 PROCEEDINGS OF THE ACL
, 2004
"... Sentiment analysis seeks to identify the viewpoint(s) underlying a text span; an example application is classifying a movie review as “thumbs up” or “thumbs down”. To determine this sentiment polarity, we propose a novel machinelearning method that applies textcategorization techniques to just the ..."
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Cited by 617 (7 self)
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the subjective portions of the document. Extracting these portions can be implemented using efficient techniques for finding minimum cuts in graphs; this greatly facilitates incorporation of crosssentence contextual constraints.
Approximating s–t Minimum Cuts in Õ(n²) Time
"... We improve on random sampling techniques for approximately solving problems that involve cuts in graphs. We give a lineartime construction that transforms any graph on n vertices into an O(n log n)edge graph on the same vertices whose cuts have approximately the same value as the original graph’s ..."
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Cited by 1 (0 self)
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’s. In this new graph, for example, we can run the Õ(mn)time maximum flow algorithm of Goldberg and Tarjan to find an s–t minimum cut in Õ(n²) time. This corresponds to a(1+)times minimum s–t cut in the original graph. In a similar way, we can approximate a sparsest cut in Õ(n²) time.
Fast approximate energy minimization via graph cuts
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2001
"... 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 v ..."
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Cited by 2121 (61 self)
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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
An Experimental Comparison of MinCut/MaxFlow Algorithms for Energy Minimization in Vision
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 2001
"... After [10, 15, 12, 2, 4] minimum cut/maximum flow algorithms on graphs emerged as an increasingly useful tool for exact or approximate energy minimization in lowlevel vision. The combinatorial optimization literature provides many mincut/maxflow algorithms with different polynomial time compl ..."
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Cited by 1313 (53 self)
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After [10, 15, 12, 2, 4] minimum cut/maximum flow algorithms on graphs emerged as an increasingly useful tool for exact or approximate energy minimization in lowlevel vision. The combinatorial optimization literature provides many mincut/maxflow algorithms with different polynomial time
What energy functions can be minimized via graph cuts?
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 2004
"... In the last few years, several new algorithms based on graph cuts have been developed to solve energy minimization problems in computer vision. Each of these techniques constructs a graph such that the minimum cut on the graph also minimizes the energy. Yet, because these graph constructions are co ..."
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Cited by 1048 (23 self)
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In the last few years, several new algorithms based on graph cuts have been developed to solve energy minimization problems in computer vision. Each of these techniques constructs a graph such that the minimum cut on the graph also minimizes the energy. Yet, because these graph constructions
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