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.
Keywords
Artificial intelligencePattern recognition (psychology)Computer scienceDensity estimationMixture modelProbabilistic logicCognitive neuroscience of visual object recognitionStatistical modelNeural codingCoding (social sciences)Computer visionFeature extractionMathematicsStatistics
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Publication Info
- Year
- 1997
- Type
- article
- Volume
- 19
- Issue
- 7
- Pages
- 696-710
- Citations
- 1443
- Access
- Closed
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Cite This
B. Moghaddam,
Alex Pentland
(1997).
Probabilistic visual learning for object representation.
IEEE Transactions on Pattern Analysis and Machine Intelligence
, 19
(7)
, 696-710.
https://doi.org/10.1109/34.598227
Identifiers
- DOI
- 10.1109/34.598227