Proceedings of the 25th international conference on Machine learning
MAP-Inference for Highly-Connected Graphs with DC-Programming
Proceedings of the 30th DAGM symposium on Pattern Recognition
Graphical Models, Exponential Families, and Variational Inference
Foundations and Trends® in Machine Learning
Iterated conditional modes for inverse dithering
Signal Processing
Interactive natural image segmentation via spline regression
IEEE Transactions on Image Processing
Generalized sparse MRF appearance models
Image and Vision Computing
Message-passing for Graph-structured Linear Programs: Proximal Methods and Rounding Schemes
The Journal of Machine Learning Research
Reparameterization based consistent graph-structured linear programs
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Optimal algorithms in multiview geometry
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Object localization based on Markov random fields and symmetry interest points
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
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SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
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This paper presents a generic method for solvingMarkov random fields (MRF) by formulating the problem of MAP estimation as 0-1 quadratic programming (QP). Though in general solving MRFs is NP-hard, we propose a second order cone programming relaxation scheme which solves a closely related (convex) approximation. In terms of computational efficiency, our method significantly outperforms the semidefinite relaxations previously used whilst providing equally (or even more) accurate results. Unlike popular inference schemes such as Belief Propagation and Graph Cuts, convergence is guaranteed within a small number of iterations. Furthermore, we also present a method for greatly reducing the runtime and increasing the accuracy of our approach for a large and useful class of MRFs. We compare our approach with the state-of-the-art methods for subgraph matching and object recognition and demonstrate significant improvements.