MRF Optimization by Graph Approximation

Wonsik Kim, Kyoung Mu Lee; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 1063-1071

Abstract


Graph cuts-based algorithms have achieved great success in energy minimization for many computer vision applications. These algorithms provide approximated solutions for multi-label energy functions via move-making approach. This approach fuses the current solution with a proposal to generate a lower-energy solution. Thus, generating the appropriate proposals is necessary for the success of the move-making approach. However, not much research efforts has been done on the generation of ``good'' proposals, especially for non-metric energy functions. In this paper, we propose an application-independent and energy-based approach to generate ``good'' proposals. With these proposals, we present a graph cuts-based move-making algorithm called GA-fusion (fusion with graph approximation-based proposals). Extensive experiments support that our proposal generation is effective across different classes of energy functions. The proposed algorithm outperforms others both on real and synthetic problems.

Related Material


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[bibtex]
@InProceedings{Kim_2015_CVPR,
author = {Kim, Wonsik and Mu Lee, Kyoung},
title = {MRF Optimization by Graph Approximation},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}
}