Many computer vision problems can be cast as an optimization problem whose feasible solutions are decompositions of a graph. The minimum cost lifted multicut problem is such an optimization problem. Its objective function can penalize or reward all decompositions for which any given pair of nodes are in distinct components. While this property has many potential applications, such applications are hampered by the fact that the problem is NP-hard. We propose a fusion move algorithm for computing feasible solutions, better and more efficiently than existing algorithms. We demonstrate this and applications to image segmentation, obtaining a new state of the art for a problem in biological image analysis.
CITATION STYLE
Beier, T., Andres, B., Köthe, U., & Hamprecht, F. A. (2016). An efficient fusion move algorithm for the minimum cost lifted multicut problem. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9906 LNCS, pp. 715–730). Springer Verlag. https://doi.org/10.1007/978-3-319-46475-6_44
Mendeley helps you to discover research relevant for your work.