The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Motion segmentation and tracking using normalized cuts
Abstract
We propose a motion segmentation algorithm that aims to break a scene into its most prominent moving groups. A weighted graph is constructed on the image sequence by connecting pixels that are in the spatiotemporal neighborhood of each other. At each pixel, we define motion profile vectors which capture the probability distribution of the image velocity. The distance between motion profiles is used to assign a weight on the graph edges. Using normalised cuts we find the most salient partitions of the spatiotemporal graph formed by the image sequence. For segmenting long image sequences, we have developed a recursive update procedure that incorporates knowledge of segmentation in previous frames for efficiently finding the group correspondence in the new frame.
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
Computation of component image velocity from local phase information
Fleet, D. J., & Jepson, A. D. (1990) · International Journal of Computer Vision · doi:10.1007/bf00056772 · Cited by 1,085
Optical flow using spatiotemporal filters
Heeger, D. J. (1988) · International Journal of Computer Vision · doi:10.1007/bf00133568 · Cited by 534
Cited by
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
Self-citationNormalized cuts and image segmentation
Shi, J., & Malik, J. (2002) · Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.1997.609407 · Cited by 849
Self-citationObject Segmentation by Long Term Analysis of Point Trajectories
Brox, T., & Malik, J. (2010) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-15555-0_21 · Cited by 744
Self-citationSegmentation of Moving Objects by Long Term Video Analysis
Ochs, P., Malik, J., & Brox, T. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.242 · Cited by 585
Self-citationModeling, Clustering, and Segmenting Video with Mixtures of Dynamic Textures
Chan, A. B., & Vasconcelos, N. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.70738 · Cited by 459
Metadata
- Authors
- Shi, J., & Malik, J.
- Venue
- Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271)
- Publisher
- Narosa Publishing House
- Published
- 2002-11-27
- Pages
- 1154-1160
- OpenAlex
- W1950703464
- Cited by
- 336
- References
- 2
- FWCI
- 15.89
- 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: Motion segmentation and tracking using normalized cuts. https://app.cochat.ai/papers/10.1109/iccv.1998.710861
- The paper
- Shi, J., & Malik, J. (2002). Motion segmentation and tracking using normalized cuts. Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271). https://doi.org/10.1109/iccv.1998.710861
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.
