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We consider move-making algorithms for energy minimization of multi-label Markov Random Fields (MRFs). Since this is not a tractable problem in general, a commonly used heuristic is to minimize over subsets of labels and variables in an iterative procedure. Such methods include {alpha}-expansion, {alpha}{beta}-swap, and range-moves. In each iteration, a small subset of variables are active in the optimization, which diminishes their effectiveness, and increases the required number of iterations. In this paper, we present a method in which optimization can be carried out over all labels, and most, or all variables at once. Experiments show substantial improvement with respect to previous move-making algorithms.
We present alpha-expansion beta-shrink moves, a simple generalization of the widely-used alpha-beta swap and alpha-expansion algorithms for approximate energy minimization. We show that in a certain sense, these moves dominate both alpha-beta-swap an
Detecting and segmenting individual objects, regardless of their category, is crucial for many applications such as action detection or robotic interaction. While this problem has been well-studied under the classic formulation of spatio-temporal gro
We contribute to approximate algorithms for the quadratic assignment problem also known as graph matching. Inspired by the success of the fusion moves technique developed for multilabel discrete Markov random fields, we investigate its applicability
We prove that the crossing changes, Delta moves, and sharp moves are unknotting operations on welded knots.
In a previous paper, the authors proved that Milnor link-homotopy invariants modulo $n$ classify classical string links up to $2n$-move and link-homotopy. As analogues to the welded case, in terms of Milnor invariants, we give here two classification