The DOI matches its Crossref record; 62 of the 62 references it cites were found in a registry or an index.
Modeling, Clustering, and Segmenting Video with Mixtures of Dynamic Textures
Abstract
A dynamic texture is a spatio-temporal generative model for video, which represents video sequences as observations from a linear dynamical system. This work studies the mixture of dynamic textures, a statistical model for an ensemble of video sequences that is sampled from a finite collection of visual processes, each of which is a dynamic texture. An expectationmaximization (EM) algorithm is derived for learning the parameters of the model, and the model is related to previous works in linear systems, machine learning, time-series clustering, control theory, and computer vision. Through experimentation, it is shown that the mixture of dynamic textures is a suitable representation for both the appearance and dynamics of a variety of visual processes that have traditionally been challenging for computer vision (e.g. fire, steam, water, vehicle and pedestrian traffic, etc.). When compared with state-of-the-art methods in motion segmentation, including both temporal texture methods and traditional representations (e.g. optical flow or other localized motion representations), the mixture of dynamic textures achieves superior performance in the problems of clustering and segmenting video of such processes.
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
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
(2012) · Encyclopedia of the Sciences of Learning · doi:10.1007/978-1-4419-1428-6_5195 · Cited by 19,370
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
(1981) · Cited by 11,963
Mean shift: a robust approach toward feature space analysis
Comaniciu, D., & Meer, P. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.1000236 · Cited by 11,305
Fundamentals of Statistical Signal Processing: Estimation Theory
Sengijpta, S. K. (1995) · Technometrics · doi:10.1080/00401706.1995.10484391 · Cited by 11,076
Horn, B. K. P., & Schunck, B. G. (1981) · Artificial Intelligence · doi:10.1016/0004-3702(81)90024-2 · Cited by 10,312
Hubert, L., & Arabie, P. (1985) · Journal of Classification · doi:10.1007/bf01908075 · Cited by 8,219
Fundamentals of statistical signal processing: Estimation theory
Prasad, K. V. (1994) · Control Engineering Practice · doi:10.1016/0967-0661(94)90195-3 · Cited by 7,902
Russell, E. L., Chiang, L. H., & Braatz, R. D. (2000) · Advances in Industrial Control · doi:10.1007/978-1-4471-0409-4_3 · Cited by 7,862
Cited by
Robust object tracking with online multiple instance learning
Babenko, B., Yang, M.-H., & Belongie, S. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.226 · Cited by 2,007
Adversarially Learned One-Class Classifier for Novelty Detection
Sabokrou, M., Khalooei, M., Fathy, M., & Adeli, E. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00356 · Cited by 765
Gong, Y., Zhang, Y., Zhu, H., Lv, J., Cheng, Q., Zhang, H., He, Y., & Wang, S. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2946059 · Cited by 101
Multi-sensor physical activity recognition in free-living
Ellis, K., Kerr, J., Godbole, S., & Lanckriet, G. (2014) · Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication · doi:10.1145/2638728.2641673 · Cited by 44
Metadata
- Authors
- Chan, A. B., & Vasconcelos, N.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2008-05
- Volume
- 30
- Issue
- 5
- Pages
- 909-926
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W2162616721
- Cited by
- 459
- References
- 62
- FWCI
- 33.41
- 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: Modeling, Clustering, and Segmenting Video with Mixtures of Dynamic Textures. https://app.cochat.ai/papers/10.1109/tpami.2007.70738
- The paper
- Chan, A. B., & Vasconcelos, N. (2008). Modeling, Clustering, and Segmenting Video with Mixtures of Dynamic Textures. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2007.70738
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.
