Diverse M-Best Solutions by Dynamic Programming
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. Note: We have observed that an assumption in section 4 of our paper is not always fulfilled, see the attached corrigendum for details.
- Publication:
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arXiv e-prints
- Pub Date:
- March 2018
- DOI:
- 10.48550/arXiv.1803.05748
- arXiv:
- arXiv:1803.05748
- Bibcode:
- 2018arXiv180305748H
- Keywords:
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- Computer Science - Computer Vision and Pattern Recognition
- E-Print:
- Includes supplementary and corrigendum