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76
Higher-Order Clique Reduction Without Auxiliary Variables
"... We introduce a method to reduce most higher-order terms of Markov Random Fields with binary labels into lower-order ones without introducing any new variables, while keeping the minimizer of the energy unchanged. While the method does not reduce all terms, it can be used with existing techniques tha ..."
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Cited by 1 (0 self)
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We introduce a method to reduce most higher-order terms of Markov Random Fields with binary labels into lower-order ones without introducing any new variables, while keeping the minimizer of the energy unchanged. While the method does not reduce all terms, it can be used with existing techniques
Solving Stereo Correspondence through Minimizing Energy Function with Higher-Order Cliques
, 2008
"... Stereo correspondence is one of the most active research areas in computer vision. Energy minimization is widely used for early vision problems, such as image restoration, segmentation and stereo correspondence. Pairwise clique is the most commonly used smoothness term of energy function, but it is ..."
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, but it is unable to capture rich statistics of natural scene. Energy function considering higher-order clique potentials can characterizes richer statistics of natural scene than pairwise clique, but it is difficult to model higher-order clique potentials and the computation for minimization is much heavier. We
P³ & beyond: Solving energies with higher order cliques
- IN COMPUTER VISION AND PATTERN RECOGNITION
, 2007
"... In this paper we extend the class of energy functions for which the optimal α-expansion and αβ-swap moves can be computed in polynomial time. Specifically, we introduce a class of higher order clique potentials and show that the expansion and swap moves for any energy function composed of these pote ..."
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Cited by 102 (17 self)
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In this paper we extend the class of energy functions for which the optimal α-expansion and αβ-swap moves can be computed in polynomial time. Specifically, we introduce a class of higher order clique potentials and show that the expansion and swap moves for any energy function composed
Nonlinearly Constrained MRFs: Exploring the Intrinsic Dimensions of Higher-Order Cliques
"... This paper introduces an efficient approach to integrating non-local statistics into the higher-order Markov Random Fields (MRFs) framework. Motivated by the observation that many non-local statistics (e.g., shape priors, color distributions) can usually be represented by a small number of parameter ..."
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Cited by 1 (1 self)
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of parameters, we reformulate the higher-order MRF model by introducing additional latent variables to represent the intrinsic dimensions of the higher-order cliques. The resulting new model, called NC-MRF, not only provides the flexibility in representing the configurations of higher-order cliques, but also
Optimizing binary MRFS with higher order cliques
, 2008
"... Abstract. Widespread use of efficient and successful solutions of Com-puter Vision problems based on pairwise Markov Random Field (MRF) models raises a question: does any link exist between the pairwise and higher order MRFs such that the like solutions can be applied to the latter models? This work ..."
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Cited by 11 (0 self)
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Abstract. Widespread use of efficient and successful solutions of Com-puter Vision problems based on pairwise Markov Random Field (MRF) models raises a question: does any link exist between the pairwise and higher order MRFs such that the like solutions can be applied to the latter models
Exact Inference in Multi-label CRFs with Higher Order Cliques
, 2008
"... This paper addresses the problem of exactly inferring the maximum a posteriori solutions of discrete multi-label MRFs or CRFs with higher order cliques. We present a framework to transform special classes of multi-label higher order functions to submodular second order boolean functions (referred to ..."
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Cited by 50 (11 self)
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This paper addresses the problem of exactly inferring the maximum a posteriori solutions of discrete multi-label MRFs or CRFs with higher order cliques. We present a framework to transform special classes of multi-label higher order functions to submodular second order boolean functions (referred
Efficient Belief Propagation for Higher Order Cliques Using Linear Constraint Nodes
, 2008
"... Belief propagation over pairwise connected Markov Random Fields has become a widely used approach, and has been successfully applied to several important computer vision problems. However, pairwise interactions are often insufficient to capture the full statistics of the problem. Higher-order intera ..."
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Cited by 8 (2 self)
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real-valued variables. We discuss how this technique can be generalized to still wider classes of potential functions at varying levels of efficiency. Also, we develop a form of nonparametric belief representation specifically designed to address issues common to networks with higher-order cliques
Efficient Belief Propagation for Higher-Order Cliques Using Linear Constraint Nodes
- COMPUTER VISION AND IMAGE UNDERSTANDING
, 2008
"... ..."
Higher-Order Clique Reduction in Binary Graph Cut Hiroshi Ishikawa
"... We introduce a new technique that can reduce any higher-order Markov random field with binary labels into a first-order one that has the same minima as the original. Moreover, we combine the reduction with the fusion-move and QPBO algorithms to optimize higher-order multi-label problems. While many ..."
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with higher-order interac-tions. Our algorithm challenges this restriction that limits the representational power of the models, so that higher-order energies can be used to capture the rich statistics of natural scenes. To demonstrate the algorithm, we minimize a third-order energy, which allows clique
Image Segmentation for Object Detection using CRFs with Robust Higher Order Clique Potentials
"... Object recognition is a fundamental problem in computer vision. In this work, an approach for object recognition that combines detection and segmentation is explored. Using the result of segmentation in the detection process leads to significant improvements in the recognition accuracies. Rather tha ..."
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than considering a simple pairwise CRF model for the segmentation process, the use of higher-order clique potentials is explored. The results are presented on the PASCAL Visual Object Classes Challenge dataset. 1
Results 1 - 10
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76
