Learning to segment from a few well-selected training images
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Active Structured Learning for High-Speed Object Detection
Proceedings of the 31st DAGM Symposium on Pattern Recognition
Max-Margin Weight Learning for Markov Logic Networks
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Kernel Methods in Computer Vision
Foundations and Trends® in Computer Graphics and Vision
Optimal Weights for Convex Functionals in Medical Image Segmentation
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Conditional random fields for object and background estimation in fluorescence video-microscopy
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
A bottom-up and top-down model for cell segmentation using multispectral data
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Optimizing complex loss functions in structured prediction
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Learning what and how of contextual models for scene labeling
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Adaptive regularization for image segmentation using local image curvature cues
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Segmenting salient objects from images and videos
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Category independent object proposals
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
On parameter learning in CRF-based approaches to object class image segmentation
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Learning an interactive segmentation system
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
Classification and Semantic Mapping of Urban Environments
International Journal of Robotics Research
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
Efficient structured support vector regression
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Global Interactions in Random Field Models: A Potential Function Ensuring Connectedness
SIAM Journal on Imaging Sciences
Fast Approximate Energy Minimization with Label Costs
International Journal of Computer Vision
Unsupervised Learning for Graph Matching
International Journal of Computer Vision
Structured Learning and Prediction in Computer Vision
Foundations and Trends® in Computer Graphics and Vision
Conditional random fields and supervised learning in automated skin lesion diagnosis
Journal of Biomedical Imaging - Special issue on Machine Learning in Medical Imaging
Segmentation of objects in a detection window by Nonparametric Inhomogeneous CRFs
Computer Vision and Image Understanding
Weakly Supervised Localization and Learning with Generic Knowledge
International Journal of Computer Vision
User-Centric Learning and Evaluation of Interactive Segmentation Systems
International Journal of Computer Vision
Learning domain knowledge for façade labelling
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
On learning higher-order consistency potentials for multi-class pixel labeling
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Taxonomic multi-class prediction and person layout using efficient structured ranking
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Structured image segmentation using kernelized features
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Computer Vision and Image Understanding
Learning graph laplacian for image segmentation
Transactions on Computational Science XIX
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Many computer vision problems are naturally formulated as random fields, specifically MRFs or CRFs. The introduction of graph cuts has enabled efficient and optimal inference in associative random fields, greatly advancing applications such as segmentation, stereo reconstruction and many others. However, while fast inference is now widespread, parameter learning in random fields has remained an intractable problem. This paper shows how to apply fast inference algorithms, in particular graph cuts, to learn parameters of random fields with similar efficiency. We find optimal parameter values under standard regularized objective functions that ensure good generalization. Our algorithm enables learning of many parameters in reasonable time, and we explore further speedup techniques. We also discuss extensions to non-associative and multi-class problems. We evaluate the method on image segmentation and geometry recognition.