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ACCV
2007
Springer

Efficiently Solving the Fractional Trust Region Problem

8 years 11 months ago
Efficiently Solving the Fractional Trust Region Problem
Normalized Cuts has successfully been applied to a wide range of tasks in computer vision, it is indisputably one of the most popular segmentation algorithms in use today. A number of extensions to this approach have also been proposed, ones that can deal with multiple classes or that can incorporate a priori information in the form of grouping constraints. It was recently shown how a general linearly constrained Normalized Cut problem can be solved. This was done by proving that strong duality holds for the Lagrangian relaxation of such problems. This provides a principled way to perform multi-class partitioning while enforcing any linear constraints exactly. The Lagrangian relaxation requires the maximization of the algebraically smallest eigenvalue over a one-dimensional matrix sub-space. This is an unconstrained, piece-wise differentiable and concave problem. In this paper we show how to solve this optimization efficiently even for very large-scale problems. The method has been tes...
Anders P. Eriksson, Carl Olsson, Fredrik Kahl
Added 12 Aug 2010
Updated 12 Aug 2010
Type Conference
Year 2007
Where ACCV
Authors Anders P. Eriksson, Carl Olsson, Fredrik Kahl
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