Reduced graphs for min-cut/max-flow approaches in image segmentation |
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| Affiliation: | 1. Department of Computer Science, P. G. Demidov Yaroslavl State University, Yaroslavl, Yaroslavl Region, Russian Federation;2. Delone Laboratory of Discrete and Computational Geometry, P. G. Demidov Yaroslavl State University, Yaroslavl, Yaroslavl Region, Russian Federation;3. Computer Science Department, IST Austria (Institute of Science and Technology Austria), Klosterneuburg, Lower Austria, Austria;4. Endoscopy Department, Yaroslavl Regional Cancer Hospital, Yaroslavl, Yaroslavl Region, Russian Federation |
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| Abstract: | In few years, min-cut/max-flow approach has become a leading method for solving a wide range of problems in computer vision. However, min-cut/max-flow approaches involve the construction of huge graphs which sometimes do not fit in memory. Currently, most of the max-flow algorithms are impracticable to solve such large scale problems. In this paper, we introduce a new strategy for reducing exactly graphs in the image segmentation context. During the creation of the graph, we test if the node is really useful to the max-flow computation. Numerical experiments validate the relevance of this technique to segment large scale images. |
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