Automated design of Bayesian perceptual inference networks

We previously presented (Sarkar and Boyer, 1993) the Perceptual Inference Network (PIN), a formalism based on Bayesian Networks, to reason among a set of object or feature hypotheses and to integrate multiple sources of information in the context of perceptual organization. The design of a PIN requires knowledge of the dependency structure among the organizations of interest and the specification of the conditional probabilities. This design was done manually with large doses of tedium and guesswork. In this paper we present an algorithm based on structural entropic measures and random parametric structural descriptions (RPSDs) to design a PIN automatically and in a (more) theoretically sound fashion. Experimental results present evidence of the robustness of the algorithm and make performance comparisons on real image data with a manually structured PIN. Since PINs are a form of Bayesian Network, we hope that this work will also prove useful towards structuring Bayesian Networks in other computer vision contexts.<
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