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Fast, Approximately Optimal Solutions for Single and Dynamic MRFs
Abstract
A new efficient MRF optimization algorithm, called Fast-PD, is proposed, which generalizes α-expansion. One of its main advantages is that it offers a substantial speedup over that method, e.g. it can be at least 3-9 times faster than α-expansion. Its efficiency is a result of the fact that Fast-PD exploits information coming not only from the original MRF problem, but also from a dual problem. Furthermore, besides static MRFs, it can also be used for boosting the performance of dynamic MRFs, i.e. MRFs varying over time. On top of that, Fast-PD makes no compromise about the optimality of its solutions: it can compute exactly the same answer as a-expansion, but, unlike that method, it can also guarantee an almost optimal solution for a much wider class of NP-hard MRF problems. Results on static and dynamic MRFs demonstrate the algorithm's efficiency and power. E.g., Fast-PD has been able to compute disparity for stereoscopic sequences in real time, with the resulting disparity coinciding with that of a-expansion.
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Cites
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Boykov, Y., Veksler, O., & Zabih, R. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.969114 · Cited by 6,988
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Metadata
- Authors
- Komodakis, N., Tziritas, G., & Paragios, N.
- Venue
- 2007 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2007-06
- Pages
- 1-8
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2149203124
- Cited by
- 185
- References
- 1
- FWCI
- 20.25
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Komodakis, N., Tziritas, G., & Paragios, N. (2007). Fast, Approximately Optimal Solutions for Single and Dynamic MRFs. 2007 IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2007.383095
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