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Additional info for Computer Vision - ECCV 2008: 10th European Conference on Computer Vision, Marseille, France, October 12-18, 2008, Proceedings, Part IV
G. [1,21]). In particular, it should be noted that the way our framework handles shape priors is related to previous approaches like  that used tree search over shape hierarchies. However, neither of those approaches accomplish pixel-wise image segmentation. 3 Optimization Framework In this section, we discuss our global energy optimization framework for obtaining image segmentations under non-local priors1 . In the next sections, we detail how it can be used for the segmentation with non-local shape priors (Section 4) and non-local intensity/color priors (Section 5).
This algorithm propagates a front of nodes in the top-down direction (Fig. 1). During the search, the front contains a set of tree nodes, such that each top-down path from the root to a leaf contains 20 V. Lempitsky, A. Blake, and C. Rother Fig. 1. Best-first branch-and-bound optimization on the tree of nested regions ﬁnds the globally-optimal ω by the top-down propagation of the active front (see text for details). At the moment when the lowest lower bound of the front is observed at leaf node, the process terminates with the global minimum found without traversing the whole tree.
These limitations are overcome in our framework, which allows the edge weights to vary. g. ). Our framework is however much more general then these methods (at a price of having higher worst-case complexity), as we allow both unary and pairwise energy terms to depend non-monotonically on a single or multiple non-local parameters. Such generality gives our framework ﬂexibility in incorporating various high-level priors while retaining the globality of the optimization. Image Segmentation by Branch-and-Mincut 17 Image segmentation with non-local shape and color priors has attracted a lot of interest in the last years.