Probabilistic image segmentation with closedness constraints

Andres, Bjoern; Kappes, Jörg Hendrik; Beier, Thorsten; Köthe, Ullrich; Hamprecht, Fred A.

doi:10.1109/iccv.2011.6126550

Cited by 89 publications

(148 citation statements)

References 24 publications

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“…Applications include unsupervised image partitioning [40,4], task-specific image partitioning [41], semantic image segmentation [52,36], and modularity clustering in network analysis [14].…”

Section: Overview Motivationmentioning

confidence: 99%

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Higher-order segmentation via multicuts

Kappes

Speth

Reinelt

et al. 2016

Computer Vision and Image Understanding

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Multicuts enable to conveniently represent discrete graphical models for unsupervised and supervised image segmentation, in the case of local energy functions that exhibit symmetries. The basic Potts model and natural extensions thereof to higher-order models provide a prominent class of such objectives, that cover a broad range of segmentation problems relevant to image analysis and computer vision. We exhibit a way to systematically take into account such higher-order terms for computational inference. Furthermore, we present results of a comprehensive and competitive numerical evaluation of a variety of dedicated cutting-plane algorithms. Our approach enables the globally optimal evaluation of a significant subset of these models, without compromising runtime. Polynomially solvable relaxations are studied as well, along with advanced rounding schemes for post-processing.

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Section: Overview Motivationmentioning

confidence: 99%

“…Major aspects of current work include closedness constraints for image segmentation [4,7], contour completion [55], ensemble segmentation [3,55], and the convex hull of feasible multicuts from the optimization point of view [35,41,69].…”

Section: Related Workmentioning

confidence: 99%

Higher-order segmentation via multicuts

Kappes

Speth

Reinelt

et al. 2016

Computer Vision and Image Understanding

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“…In computer vision, this combinatorial optimization problem has been used to formalize image segmentation. To date, multicuts of superpixel adjacency graphs [2,17] are among the closest, in terms of partition metrics, to the manmade segmentations in the Berkeley Segmentation Benchmark [4].…”

Section: Related Workmentioning

confidence: 99%

“…The formalization of the image segmentation problem as a multicut problem has recently attracted considerable attention [2,3,6,9,10,15,17]. This problem consists in finding a partition of a weighted superpixel adjacency graph into connected components (segments) such that the set of edges that straddle different segments (the multicut) has minimum total weight.…”

Section: Introductionmentioning

confidence: 99%

Segmenting Planar Superpixel Adjacency Graphs w.r.t. Non-planar Superpixel Affinity Graphs

Andres

Yarkony

Manjunath

et al. 2013

Lecture Notes in Computer Science

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Abstract. We address the problem of segmenting an image into a previously unknown number of segments from the perspective of graph partitioning. Specifically, we consider minimum multicuts of superpixel affinity graphs in which all affinities between non-adjacent superpixels are negative. We propose a relaxation by Lagrangian decomposition and a constrained set of re-parameterizations for which we can optimize exactly and efficiently. Our contribution is to show how the planarity of the adjacency graph can be exploited if the affinity graph is non-planar. We demonstrate the effectiveness of this approach in user-assisted image segmentation and show that the solution of the relaxed problem is fast and the relaxation is tight in practice.

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“…The majority of the currently published applications using OpenGM are directed towards 2-D and 3-D image and structure modelling. One such application is image segmentation, which attempts to partition an image into sets of like colors [4]. Fig.…”

Section: Factor Graph Softwarementioning

confidence: 99%

A hardware generator for factor graph applications

Demma

Athanas

2014

2014 International Conference on ReConFigurable Computing and FPGAs (ReConFig14)

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A Factor Graph (FG --http://en.wikipedia.org/wiki/Factor_graph) is a structure used to find solutions to problems that can be represented as a Probabilistic Graphical Model (PGM). They consist of interconnected variable nodes and factor nodes, which iteratively compute and pass messages to each other. FG's can be applied to solve decoding of forward error correcting codes, Markov chains and Markov Random Fields, Kalman Filtering, Fourier Transforms, and even some games such as Sudoku. In this paper, a framework is presented for rapid prototyping of hardware implementations of FG-based applications. The FG developer specifies aspects of the application, such as graphical structure, factor computation, and message passing algorithm, and the framework returns a design. A system of Python scripts and Verilog IntroductionProbabilistic Graphical Models (PGM) can be used to describe a broad scope of problems. They may be applied to speech recognition, computer vision, information coding, machine learning, and protein modeling, to name a few. At their most basic, PGMs are sets of random variables with conditional interdependencies. They are graphical, in that they can be modeled as connected graphs, with the random variables as vertices and the conditional dependencies as edges. A Factor Graph (FG) is a variation of a PGM that executes an algorithm to find a solution to a set of constraints, given some initial a priori information. In terms of the PGM examples listed, a factor graph might be used to map recorded voice audio to a known words, or to discern objects from image data, or to fix errors in an encoded set of bits.Factor graphs work in the domain of probability. A FG solver may be thought of as a probability processor. Elements within a FG communicate with one another by passing messages about what they believe they are, what they believe their neighbors to be, and how strongly they believe it. They don't work deductively, in serial fashion. They process iteratively and in parallel, converging their beliefs on a solution. Some problems 1 that are difficult to solve deterministically, such as those described above, can be solved probabilistically, using factor graphs. Motivation and ContributionThis work presents a system for generating factor graph solvers. It was inspired by the current state of the art of hardware FG applications, and attempts to further it with two of its components. The first is abstract; it is a generalized model for hardware FG solvers.Analysis of hardware FG applications led to the realization that their structures and hierarchies could be represented by a single model. This model is the foundation upon which the FG-Solver modules of this work are constructed. The second component is more concrete. It is the Generator Framework itself, a software system for automatically generating hardware FG solver designs. Its intended benefits are ease of creation and rapid prototyping. The system has been applied to real problems, and the generated designs have withstood thorough testing and verifica...

show abstract

Probabilistic image segmentation with closedness constraints

Cited by 89 publications

References 24 publications

Higher-order segmentation via multicuts

Higher-order segmentation via multicuts

Segmenting Planar Superpixel Adjacency Graphs w.r.t. Non-planar Superpixel Affinity Graphs

A hardware generator for factor graph applications

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