Abstract
Many computer vision pipelines involve dynamic programming primitives such as finding a shortest path or the minimum energy solution in a tree-shaped probabilistic graphical model. In such cases, extracting not merely the best, but the set of M-best solutions is useful to generate a rich collection of candidate proposals that can be used in downstream processing. In this work, we show how M-best solutions of tree-shaped graphical models can be obtained by dynamic programming on a special graph with M layers. The proposed multi-layer concept is optimal for searching M-best solutions, and so flexible that it can also approximate M-best diverse solutions. We illustrate the usefulness with applications to object detection, panorama stitching and centerline extraction.
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Notes
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See the Supplementary for an application to depth estimation from stereo.
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Acknowledgements
This work was partially supported by the HGS MathComp Graduate School, DFG grant HA 4364/9-1, SFB 1129 for integrative analysis of pathogen replication and spread, and the Swiss National Science Foundation under Grant 200020_162343/1.
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Haubold, C., Uhlmann, V., Unser, M., Hamprecht, F.A. (2017). Diverse M-Best Solutions by Dynamic Programming. In: Roth, V., Vetter, T. (eds) Pattern Recognition. GCPR 2017. Lecture Notes in Computer Science(), vol 10496. Springer, Cham. https://doi.org/10.1007/978-3-319-66709-6_21
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