We address the problem of segmenting an image into a previously unknown number of segments from the perspective of graph partitioning. Specifically, we consider minimum multicuts of superpixel affinity graphs in which all affinities between non-adjacent superpixels are negative. We propose a relaxation by Lagrangian decomposition and a constrained set of re-parameterizations for which we can optimize exactly and efficiently. Our contribution is to show how the planarity of the adjacency graph can be exploited if the affinity graph is non-planar. We demonstrate the effectiveness of this approach in user-assisted image segmentation and show that the solution of the relaxed problem is fast and the relaxation is tight in practice. © 2013 Springer-Verlag.
CITATION STYLE
Andres, B., Yarkony, J., Manjunath, B. S., Kirchhoff, S., Turetken, E., Fowlkes, C. C., & Pfister, H. (2013). Segmenting planar superpixel adjacency graphs w.r.t. non-planar superpixel affinity graphs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8081 LNCS, pp. 266–279). https://doi.org/10.1007/978-3-642-40395-8_20
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