The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
Fast approximate energy minimization via graph cuts
Abstract
In this paper we address the problem of minimizing a large class of energy functions that occur in early vision. The major restriction is that the energy function's smoothness term must only involve pairs of pixels. We propose two algorithms that use graph cuts to compute a local minimum even when very large moves are allowed. The first move we consider is an /spl alpha/-/spl beta/-swap: for a pair of labels /spl alpha/,/spl beta/, this move exchanges the labels between an arbitrary set of pixels labeled a and another arbitrary set labeled /spl beta/. Our first algorithm generates a labeling such that there is no swap move that decreases the energy. The second move we consider is an /spl alpha/-expansion: for a label a, this move assigns an arbitrary set of pixels the label /spl alpha/. Our second algorithm, which requires the smoothness term to be a metric, generates a labeling such that there is no expansion move that decreases the energy. Moreover, this solution is within a known factor of the global minimum. We experimentally demonstrate the effectiveness of our approach on image restoration, stereo and motion.
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
Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.
Geman, S., & Geman, D. (1984) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1984.4767596 · Cited by 17,949
Kass, M., Witkin, A., & Terzopoulos, D. (1988) · International Journal of Computer Vision · doi:10.1007/bf00133570 · Cited by 17,070
Normalized cuts and image segmentation
Shi, J., & Malik, J. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.868688 · Cited by 15,502
Network Flows: Theory, Algorithms, and Applications.
Smith, D. K., Ahuja, R. K., Magnanti, T. L., & Orlin, J. B. (1994) · The Journal of the Operational Research Society · doi:10.2307/2583863 · Cited by 8,034
Horn, B. K., & Schunck, B. G. (1981) · SPIE Proceedings · doi:10.1117/12.965761 · Cited by 7,394
Spatial Interaction and the Statistical Analysis of Lattice Systems
Besag, J. (1974) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1974.tb00999.x · Cited by 6,067
On the Statistical Analysis of Dirty Pictures
Besag, J. (1986) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1986.tb01412.x · Cited by 4,191
Ford, D. R., & Fulkerson, D. R. (1962) · Cited by 4,072
Nonuniversal critical dynamics in Monte Carlo simulations
Swendsen, R. H., & Wang, J.-S. (1987) · Physical Review Letters · doi:10.1103/physrevlett.58.86 · Cited by 2,844
Some generalized order-disorder transformations
Potts, R. B. (1952) · Mathematical Proceedings of the Cambridge Philosophical Society · doi:10.1017/s0305004100027419 · Cited by 2,122
Cited by
A Database and Evaluation Methodology for Optical Flow
Baker, S., Scharstein, D., Lewis, J. P., Roth, S., Black, M. J., & Szeliski, R. (2011) · International Journal of Computer Vision · doi:10.1007/s11263-010-0390-2 · Cited by 2,205
ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation
Lin, D., Dai, J., Jia, J., He, K., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.344 · Cited by 1,129
Computing visual correspondence with occlusions using graph cuts
Kolmogorov, V., & Zabih, R. (2002) · Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 · doi:10.1109/iccv.2001.937668 · Cited by 1,119
Self-citationA taxonomy and evaluation of dense two-frame stereo correspondence algorithms
Scharstein, D., Szeliski, R., & Zabih, R. (2002) · Proceedings IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV 2001) · doi:10.1109/smbv.2001.988771 · Cited by 1,084
Self-citationGraphcut textures: image and video synthesis using graph cuts
Kwatra, V., Schödl, A., Essa, I., Turk, G., & Bobick, A. (2003) · ACM Transactions on Graphics · doi:10.1145/882262.882264 · Cited by 787
Metadata
- Authors
- Boykov, Y., Veksler, O., & Zabih, R.
- Venue
- Proceedings of the Seventh IEEE International Conference on Computer Vision
- Publisher
- IEEE
- Published
- 1999
- Pages
- 377-384 vol.1
- OpenAlex
- W2148852318
- Cited by
- 470
- References
- 46
- FWCI
- 14.45
- Type
- conference paper
- 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: Fast approximate energy minimization via graph cuts. https://app.cochat.ai/papers/10.1109/iccv.1999.791245
- The paper
- Boykov, Y., Veksler, O., & Zabih, R. (1999). Fast approximate energy minimization via graph cuts. Proceedings of the Seventh IEEE International Conference on Computer Vision. https://doi.org/10.1109/iccv.1999.791245
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.
