A probabilistic model for image representation via multiple patterns

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.

► A probabilistic extension to PCA is proposed to alleviate small sample size problem.
► We explicitly represent how to transform an image into multiple input samples to PCA.
► Learning the model helps identify image regions that are well or poorly fit PCA.
► The information extracted by the model help represent image data more efficiently.