The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
An iterative optimization approach for unified image segmentation and matting
Abstract
Separating a foreground object from the background in a static image involves determining both full and partial pixel coverages, also known as extracting a matte. Previous approaches require the input image to be presegmented into three regions: foreground, background and unknown, which are called a trimap. Partial opacity values are then computed only for pixels inside the unknown region. This presegmentation based approach fails for images with large portions of semitransparent foreground where the trimap is difficult to create even manually. In this paper, we combine the segmentation and matting problem together and propose a unified optimization approach based on belief propagation. We iteratively estimate the opacity value for every pixel in the image, based on a small sample of foreground and background pixels marked by the user. Experimental results show that compared with previous approaches, our method is more efficient to extract high quality mattes for foregrounds with significant semitransparent regions
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
GrabCut: Interactive foreground extraction using iterated graph cuts
Rother, C., Kolmogorov, V., & Blake, A. (2004) · ACM Transactions on Graphics · doi:10.1145/1015706.1015720 · Cited by 5,703
Stereo matching using belief propagation
Sun, J., Zheng, N.-N., & Shum, H.-Y. (2003) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2003.1206509 · Cited by 1,271
Li, Y., Sun, J., Tang, C.-K., & Shum, H.-Y. (2004) · ACM Transactions on Graphics · doi:10.1145/1015706.1015719 · Cited by 1,097
"GrabCut": interactive foreground extraction using iterated graph cuts
Rother, C., Kolmogorov, V., & Blake, A. (2004) · ACM SIGGRAPH 2004 Papers · doi:10.1145/1186562.1015720 · Cited by 935
A Bayesian approach to digital matting
Chuang, Y.-Y., Curless, B., Salesin, D. H., & Szeliski, R. (2005) · Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001 · doi:10.1109/cvpr.2001.990970 · Cited by 796
Sun, J., Jia, J., Tang, C.-K., & Shum, H.-Y. (2004) · ACM Transactions on Graphics · doi:10.1145/1015706.1015721 · Cited by 583
On the optimality of solutions of the max-product belief-propagation algorithm in arbitrary graphs
Weiss, Y., & Freeman, W. T. (2001) · IEEE Transactions on Information Theory · doi:10.1109/18.910585 · Cited by 581
Efficient belief propagation for early vision
Felzenszwalb, P. F., & Huttenlocher, D. P. (2004) · Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. · doi:10.1109/cvpr.2004.1315041 · Cited by 442
Smith, A. R., & Blinn, J. F. (1996) · Proceedings of the 23rd annual conference on Computer graphics and interactive techniques · doi:10.1145/237170.237263 · Cited by 404
Alpha estimation in natural images
Ruzon, M. A., & Tomasi, C. (2002) · Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662) · doi:10.1109/cvpr.2000.855793 · Cited by 348
Cited by
A Closed-Form Solution to Natural Image Matting
Levin, A., Lischinski, D., & Weiss, Y. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1177 · Cited by 1,616
A Geodesic Framework for Fast Interactive Image and Video Segmentation and Matting
Bai, X., & Sapiro, G. (2007) · 2007 IEEE 11th International Conference on Computer Vision · doi:10.1109/iccv.2007.4408931 · Cited by 438
A global sampling method for alpha matting
He, K., Rhemann, C., Rother, C., Tang, X., & Sun, J. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995495 · Cited by 310
Metadata
- Authors
- Wang, J., & Cohen, M. F.
- Venue
- Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1
- Publisher
- IEEE
- Published
- 2005
- Pages
- 936-943 Vol. 2
- OpenAlex
- W2149860324
- Cited by
- 316
- References
- 15
- FWCI
- 19.54
- 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: An iterative optimization approach for unified image segmentation and matting. https://app.cochat.ai/papers/10.1109/iccv.2005.37
- The paper
- Wang, J., & Cohen, M. F. (2005). An iterative optimization approach for unified image segmentation and matting. Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1. https://doi.org/10.1109/iccv.2005.37
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.
