A fast parametric maximum flow algorithm and applications
SIAM Journal on Computing
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminative Learning of Max-Sum Classifiers
The Journal of Machine Learning Research
A Probabilistic Segmentation Scheme
Proceedings of the 30th DAGM symposium on Pattern Recognition
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In this article we propose a method for parameter learning within the energy minimisation framework for segmentation. We do this in an incremental way where user input is required for resolving segmentation ambiguities. Whereas most other interactive learning approaches focus on learning appearance characteristics only, our approach is able to cope with learning prior terms; in particular the Potts terms in binary image segmentation. The artificial as well as real examples illustrate the applicability of the approach.