Li, J. and Tao, D. and Li, Xuelong (2012) A probabilistic model for image representation via multiple patterns. Pattern Recognition 45 (11), pp. 4044-4053. ISSN 0031-3203.
Abstract
For image analysis, an important extension to principal component analysis (PCA) is to treat an image as multiple samples, which helps alleviate the small sample size problem. Various schemes of transforming an image to multiple samples have been proposed. Although having been shown effective in practice, the schemes are mainly based on heuristics and experience. In this paper, we propose a probabilistic PCA model, in which we explicitly represent the transformation scheme and incorporate the scheme as a stochastic component of the model. Therefore fitting the model automatically learns the transformation. Moreover, the learned model allows us to distinguish regions that can be well described by the PCA model from those that need further treatment. Experiments on synthetic images and face data sets demonstrate the properties and utility of the proposed model.
Metadata
| Item Type: | Article |
|---|---|
| Keyword(s) / Subject(s): | principal component analysis, probabilistic model |
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Depositing User: | Sarah Hall |
| Date Deposited: | 07 Jun 2013 09:06 |
| Last Modified: | 09 Aug 2023 12:33 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/7364 |
Statistics
Additional statistics are available via IRStats2.
Archive Staff Only (login required)
