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.
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Hamarneh, G. (2009). Multi-label MRF Optimization via a Least Squares s − t Cut. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2009. Lecture Notes in Computer Science, vol 5875. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10331-5_98
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DOI: https://doi.org/10.1007/978-3-642-10331-5_98
Publisher Name: Springer, Berlin, Heidelberg
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