Compositionality, MDL Priors, and Object Recognition

Abstract

Images are ambiguous at each of many levels of a contextual hierarchy. Nevertheless, the high-level interpretation of most scenes is unambiguous, as evidenced by the superior performance of humans. This observation argues for global vision models, such as deformable templates. Unfortunately, such models are computationally intractable for unconstrained problems. We propose a compositional model in which primitives are recursively composed, subject to syntactic restrictions, to form tree-structured objects and object groupings. Ambiguity is propagated up the hierarchy in the form of multiple interpretations, which are later resolved by a Bayesian, equivalently minimum-description-length, cost functional. 1 Bayesian decision theory and compositionality In his Essay on Probability, Laplace (1812) devotes a short chapter---his "Sixth Principle"---to what we call today the Bayesian decision rule. Laplace observes that we interpret a "regular combination," e.g., an arrangement of objects th...