Recursive Compositional Models for Vision: Description and Review of Recent Work

Abstract

This paper describes and reviews a class of hierarchical probabilistic models of images and objects. Visual structures are represented in a hierarchical form where complex structures are composed of more elementary structures following a design principle of recursive composition. Probabilities are defined over these structures which exploit properties of the hierarchy--e.g. long range spatial relationships can be represented by local potentials at the upper levels of the hierarchy. The compositional nature of this representation enables efficient learning and inference algorithms. In particular, parts can be shared between different object models. Overall the architecture of Recursive Compositional Models (RCMs) provides a balance between statistical and computational complexity. The goal of this paper is to describe the basic ideas and common themes of RCMs, to illustrate their success on a range of vision tasks, and to gives pointers to the literature. In particular, we show that RCMs generally give state of the art results when applied to a range of different vision tasks and evaluated on the leading benchmarked datasets.