Multi-label MRF Optimization via a Least Squares s - t Cut

Authors:
Ghassan Hamarneh
Affiliations:
School of Computing Science, Simon Fraser University, Canada
Venue:
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Year:
2009

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Abstract

We approximate the k -label Markov random field optimization by a single binary (s - t ) graph cut. Each vertex in the original graph is replaced by only ceil (log 2 (k )) new vertices and the new edge weights are obtained via a novel least squares solution approximating the original data and label interaction penalties. The s - t cut produces a binary "Gray" encoding that is unambiguously decoded into any of the original k labels. We analyze the properties of the approximation and present quantitative and qualitative image segmentation results, one of the several computer vision applications of multi label-MRF optimization.