The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Probabilistic visual learning for object representation
Abstract
We present an unsupervised technique for visual learning, which is based on density estimation in high-dimensional spaces using an eigenspace decomposition. Two types of density estimates are derived for modeling the training data: a multivariate Gaussian (for unimodal distributions) and a mixture-of-Gaussians model (for multimodal distributions). Those probability densities are then used to formulate a maximum-likelihood estimation framework for visual search and target detection for automatic object recognition and coding. Our learning technique is applied to the probabilistic visual modeling, detection, recognition, and coding of human faces and nonrigid objects, such as hands.
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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
Golub, G. (2012) · doi:10.56021/9781421407944 · Cited by 34,505
Turk, M., & Pentland, A. (1991) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1991.3.1.71 · Cited by 13,733
Self-citation
Cited by
The FERET evaluation methodology for face-recognition algorithms
Phillips, P. J., Moon, H., Rizvi, S. A., & Rauss, P. J. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.879790 · Cited by 3,692
Martinez, A. M., & Kak, A. C. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.908974 · Cited by 3,249
Separating Style and Content with Bilinear Models
Tenenbaum, J. B., & Freeman, W. T. (2000) · Neural Computation · doi:10.1162/089976600300015349 · Cited by 840
Deformable Model Fitting by Regularized Landmark Mean-Shift
Saragih, J. M., Lucey, S., & Cohn, J. F. (2011) · International Journal of Computer Vision · doi:10.1007/s11263-010-0380-4 · Cited by 787
Visual tracking and recognition using appearance-adaptive models in particle filters
Zhou, S. K., Chellappa, R., & Moghaddam, B. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2004.836152 · Cited by 668
Self-citationFloatBoost learning and statistical face detection
Li, S. Z., & Zhang, Z. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.68 · Cited by 525
Machine Analysis of Facial Expressions
Pantic, M., & Stewart, M. (2007) · Face Recognition · doi:10.5772/4847 · Cited by 248
A Study of Face Recognition as People Age
Ling, H., Soatto, S., Ramanathan, N., & Jacobs, D. W. (2007) · 2007 IEEE 11th International Conference on Computer Vision · doi:10.1109/iccv.2007.4409069 · Cited by 101
Metadata
- Authors
- Moghaddam, B., & Pentland, A.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 1997-07
- Volume
- 19
- Issue
- 7
- Pages
- 696-710
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2128716185
- Cited by
- 1,441
- References
- 3
- FWCI
- 48.54
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
