The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Minimizing Nonsubmodular Functions with Graph Cuts-A Review
Abstract
Optimization techniques based on graph cuts have become a standard tool for many vision applications. These techniques allow to minimize efficiently certain energy functions corresponding to pairwise Markov Random Fields (MRFs). Currently, there is an accepted view within the computer vision community that graph cuts can only be used for optimizing a limited class of MRF energies (e.g., submodular functions). In this survey, we review some results that show that graph cuts can be applied to a much larger class of energy functions (in particular, nonsubmodular functions). While these results are well-known in the optimization community, to our knowledge they were not used in the context of computer vision and MRF optimization. We demonstrate the relevance of these results to vision on the problem of binary texture restoration.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Fast approximate energy minimization via graph cuts
Boykov, Y., Veksler, O., & Zabih, R. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.969114 · Cited by 6,988
Boros, E., & Hammer, P. L. (2002) · Discrete Applied Mathematics · doi:10.1016/s0166-218x(01)00341-9 · Cited by 827
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
Self-citationA study on continuous max-flow and min-cut approaches
Yuan, J., Bae, E., & Tai, X.-C. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5539903 · Cited by 237
Semantic segmentation of urban scenes by learning local class interactions
Volpi, M., & Ferrari, V. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2015.7301377 · Cited by 152
Transformation of General Binary MRF Minimization to the First-Order Case
Ishikawa, H. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.91 · Cited by 144
Metadata
- Authors
- Kolmogorov, V., & Rother, C.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2007-07
- Volume
- 29
- Issue
- 7
- Pages
- 1274-1279
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2153396823
- Cited by
- 362
- References
- 2
- FWCI
- 29.67
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Minimizing Nonsubmodular Functions with Graph Cuts-A Review. https://app.cochat.ai/papers/10.1109/tpami.2007.1031
- The paper
- Kolmogorov, V., & Rother, C. (2007). Minimizing Nonsubmodular Functions with Graph Cuts-A Review. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2007.1031
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
