A Bayesian hierarchical factorization model for vector fields.

Abstract

Factorization-based techniques explain arrays of observations using a relatively small number of factors and provide an essential arsenal for multi-dimensional data analysis. Most factorization models are, however, developed on general arrays of scalar values. For a class of practical data arising from observing spatial signals including images, it is desirable for a model to consider general observations, e.g., handling a vector field and non-exchangeable factors, e.g., handling spatial connections between the columns and the rows of the data. In this paper, a probabilistic model for factorization is proposed. We adopt Bayesian hierarchical modeling and treat the factors as latent random variables. A Markov structure is imposed on the distribution of factors to account for the spatial connections. The model is designed to represent vector arrays sampled from fields of continuous domains. Therefore, a tailored observation model is developed to represent the link between the factor product and the data. The proposed technique has been shown effective in analyzing optical flow fields computed on both synthetic images and real-life videoclips.