Multi-label MRF Optimization via a Least Squares s - t Cut
نویسنده
چکیده
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(log2(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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تاریخ انتشار 2009