Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Experimental Comparison of Min-cut/Max-flow Algorithms for Energy Minimization in Vision
EMMCVPR '01 Proceedings of the Third International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
A New Framework for Approximate Labeling via Graph Cuts
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
An Experimental Comparison of Discrete and Continuous Shape Optimization Methods
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Dynamic Hybrid Algorithms for MAP Inference in Discrete MRFs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast and exact primal-dual iterations for variational problems in computer vision
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Interactive motion segmentation
Proceedings of the 32nd DAGM conference on Pattern recognition
Interactive multi-label segmentation
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
A comparative study of energy minimization methods for markov random fields
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Continuous Multiclass Labeling Approaches and Algorithms
SIAM Journal on Imaging Sciences
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State-of-the-art approaches in interactive image segmentation often fail for objects exhibiting complex color variability, similar colors or difficult lighting conditions. The reason is that they treat the given user information as independent and identically distributed in the input space yielding a single color distribution per region. Due to their strong overlap segmentation often fails. By statistically taking into account the local distribution of the scribbles we obtain spatially varying color distributions, which are locally separable and allow for weaker regularization assumptions. Starting from a Bayesian formulation for image segmentation, we derive a variational framework for multiregion segmentation, which incorporates spatially adaptive probability density functions. Minimization is done by three different optimization methods from the MRF and PDE community. We discuss advantages and drawbacks of respective algorithms and compare them experimentally in terms of segmentation accuracy, quantitative performance on the Graz benchmark and speed.