Rounding-based Moves for Metric Labeling

Abstract:

Metric labeling is an important special case of energy minimization in pairwise graphical models. The dominant methods for metric labeling in the computer vision community belong to the move-making family, due to their computational efficiency. The dominant methods in the computer science community belong to the convex relaxations family, due to their strong theoretical guarantees. In this talk, I will present algorithms that combine the best of both worlds: efficient move-making algorithms that provide the same guarantees as the standard linear programming relaxation.

Brief Bio:

M. Pawan Kumar is an Assistant Professor at Ecole Centrale Paris, and a member of INRIA Saclay. Prior to that, he was a PhD student at Oxford Brookes University (Brookes Vision Group; 2003-2007), a postdoc at Oxford University (Visual Geometry Group; 2008), and a postdoc at Stanford University (Daphne's Approximate Group of Students; 2009-2011).