A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
International Journal of Computer Vision
Visual Correspondence Using Energy Minimization and Mutual Information
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Computing Geodesics and Minimal Surfaces via Graph Cuts
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Convex Optimization
"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
Cosegmentation of Image Pairs by Histogram Matching - Incorporating a Global Constraint into MRFs
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Shape Representation and Classification Using the Poisson Equation
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Total Variation Minimization and Surface Evolution Using Parametric Maximum Flows
International Journal of Computer Vision
A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging
Journal of Mathematical Imaging and Vision
An integral solution to surface evolution PDEs via geo-cuts
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Total variation minimization and a class of binary MRF models
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Active contours for tracking distributions
IEEE Transactions on Image Processing
Global-local optimizations by hierarchical cuts and climbing energies
Pattern Recognition
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This paper is concerned with energy-based image segmentation problems. We introduce a general class of regional functionals defined as an arbitrary non-linear combination of regional unary terms. Such (high-order) functionals are very useful in vision and medical applications and some special cases appear in prior art. For example, our general class of functionals includes but is not restricted to soft constraints on segment volume, its appearance histogram, or shape. Our overall segmentation energy combines regional functionals with standard length-based regularizers and/or other submodular terms. In general, regional functionals make the corresponding energy minimization NP-hard. We propose a new greedy algorithm based on iterative line search. A parametric max-flow technique efficiently explores all solutions along the direction (line) of the steepest descent of the energy. We compute the best "step size", i.e. the globally optimal solution along the line. This algorithm can make large moves escaping weak local minima, as demonstrated on many real images.