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ICCV
2007
IEEE
14 years 10 months ago
Multi-Object Tracking Through Clutter Using Graph Cuts
The standard graph cut technique is a robust method for globally optimal image segmentations. However, because of its global nature, it is prone to capture outlying areas similar ...
Allen Tannenbaum, James G. Malcolm, Yogesh Rathi
CVPR
2010
IEEE
13 years 8 months ago
Learning kernels for variants of normalized cuts: Convex relaxations and applications
We propose a new algorithm for learning kernels for variants of the Normalized Cuts (NCuts) objective – i.e., given a set of training examples with known partitions, how should ...
Lopamudra Mukherjee, Vikas Singh, Jiming Peng, Chr...
OL
2011
177views Neural Networks» more  OL 2011»
12 years 11 months ago
Exploiting vector space properties to strengthen the relaxation of bilinear programs arising in the global optimization of proce
In this paper we present a methodology for finding tight convex relaxations for a special set of quadratic constraints given by bilinear and linear terms that frequently arise in ...
Juan P. Ruiz, Ignacio E. Grossmann

Presentation
1717views
15 years 7 months ago
Global vs. Local Optimisation Algorithms
A detailed presentation by Yuri Boykov, Daniel Cremers, Vladimir Kolmogorov, at the European Conference on Computer Vision (ECCV) 2006, that shows the difference between local and ...
ECCV
2008
Springer
14 years 10 months ago
Image Segmentation by Branch-and-Mincut
Efficient global optimization techniques such as graph cut exist for energies corresponding to binary image segmentation from lowlevel cues. However, introducing a high-level prior...
Victor S. Lempitsky, Andrew Blake, Carsten Rother