Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Training structural SVMs when exact inference is intractable
Proceedings of the 25th international conference on Machine learning
Training structural svms with kernels using sampled cuts
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Kernel Codebooks for Scene Categorization
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Learning CRFs Using Graph Cuts
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
International Journal of Computer Vision
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
Fast support vector machines for structural Kernels
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Efficient Additive Kernels via Explicit Feature Maps
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernelized structural SVM learning for supervised object segmentation
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Are spatial and global constraints really necessary for segmentation?
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
SLIC Superpixels Compared to State-of-the-Art Superpixel Methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparse reconstruction for weakly supervised semantic segmentation
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Most state-of-the-art approaches to image segmentation formulate the problem using Conditional Random Fields. These models typically include a unary term and a pairwise term, whose parameters must be carefully chosen for optimal performance. Recently, structured learning approaches such as Structured SVMs (SSVM) have made it possible to jointly learn these model parameters. However, they have been limited to linear kernels, since more powerful non-linear kernels cause the learning to become prohibitively expensive. In this paper, we introduce an approach to "kernelize" the features so that a linear SSVM framework can leverage the power of non-linear kernels without incurring the high computational cost. We demonstrate the advantages of this approach in a series of image segmentation experiments on the MSRC data set as well as 2D and 3D datasets containing imagery of neural tissue acquired with electron microscopes.