The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Dynamic Graph Cuts for Efficient Inference in Markov Random Fields
Abstract
Abstract-In this paper we present a fast new fully dynamic algorithm for the st-mincut/max-flow problem. We show how this algorithm can be used to efficiently compute MAP solutions for certain dynamically changing MRF models in computer vision such as image segmentation. Specifically, given the solution of the max-flow problem on a graph, the dynamic algorithm efficiently computes the maximum flow in a modified version of the graph. The time taken by it is roughly proportional to the total amount of change in the edge weights of the graph. Our experiments show that, when the number of changes in the graph is small, the dynamic algorithm is significantly faster than the best known static graph cut algorithm. We test the performance of our algorithm on one particular problem: the object-background segmentation problem for video. It should be noted that the application of our algorithm is not limited to the above problem, the algorithm is generic and can be used to yield similar improvements in many other cases that involve dynamic change.
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Cites
Boros, E., & Hammer, P. L. (2002) · Discrete Applied Mathematics · doi:10.1016/s0166-218x(01)00341-9 · Cited by 827
Energy Minimization via Graph Cuts: Settling What is Possible
Freedman, D., & Drineas, P. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.143 · Cited by 173
Cited by
Optical flow modeling and computation: A survey
Fortun, D., Bouthemy, P., & Kervrann, C. (2015) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2015.02.008 · Cited by 431
A Comparative Study of Energy Minimization Methods for Markov Random Fields
Szeliski, R., Zabih, R., Scharstein, D., Veksler, O., Kolmogorov, V., Agarwala, A., Tappen, M., & Rother, C. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744047_2 · Cited by 386
GeoS: Geodesic Image Segmentation
Criminisi, A., Sharp, T., & Blake, A. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-88682-2_9 · Cited by 277
Metadata
- Authors
- Kohli, P., & Torr, P. H. S.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2007-12
- Volume
- 29
- Issue
- 12
- Pages
- 2079-2088
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2126789858
- Cited by
- 191
- References
- 2
- FWCI
- 24.00
- Type
- journal article
- Language
- en
- Open access
- closed
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- Kohli, P., & Torr, P. H. S. (2007). Dynamic Graph Cuts for Efficient Inference in Markov Random Fields. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2007.1128
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