PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Solving markov random fields using second order cone programming relaxations
Mudigonda Pawan Kumar, Philip Torr and Andrew Zisserman
In: CVPR 2006, 17-22 June 2006, New York, USA.

Abstract

This paper presents a generic method for solving Markov random fields (MRF) by formulating the problem of MAP estimation as 0-1 quadratic programming (QP). Though in general solving MRFs is NP-hard, we propose a second order cone programming relaxation scheme which solves a closely related (convex) approximation. In terms of computational efficiency, our method significantly outperforms the semidefinite relaxations previously used whilst providing equally (or even more) accurate results. Unlike popular inference schemes such as Belief Propagation and Graph Cuts, convergence is guaranteed within a small number of iterations. Furthermore, we also present a method for greatly reducing the runtime and increasing the accuracy of our approach for a large and useful class of MRF. We compare our approach with the state-of-the-art methods for subgraph matching and object recognition and demonstrate significant improvements.

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EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:2113
Deposited By:Mudigonda Pawan Kumar
Deposited On:21 May 2006