We present a probabilistic approach to learning object representations based on the "content and style" bilinear generative model of Tenenbaum and Freeman. In contrast to their earlier SVD-based approach, our approach models images using particle filters. We maintain separate particle filters to represent the content and style spaces, allowing us to define arbitrary weighting functions over the particles to help estimate the content/style densities. We combine this approach with a new EM-based method for learning basis vectors that describe content-style mixing. Using a particle-based representation permits good reconstruction despite reduced dimensionality, and increases storage capacity and computational efficiency. We describe how learning the distributions using particle filters allows us to efficiently compute a probabilistic "novelty" term. Our example application considers a dataset of faces under different lighting conditions. The system classifies faces of people it has seen before, and can identify previously unseen faces as new content. Using a probabilistic definition of novelty in conjunction with learning content-style separability provides a crucial building block for designing real-world, real-time object recognition systems.