Recognition and learning with polymorphic structural components

We address the problem of describing, recognizing, and learning generic, free-form objects in real-world scenes. An appearance-based system using weak-structure and evidence accumulation where object models are implicitly encoded in a learned decision tree and objects are represented in graph form as in the method developed by Bischof and Caelli (1994). The decision tree is used to classify sequences of image components, or part paths, extracted from the object to be recognized. The part paths are in turn used to accumulate evidence for the classification of the entire object. We introduce an improved method for generating part paths based upon the part compatibility graph, a replacement for Bischof and Caelli's implicit use of the part adjacency graph. A new formalism for extending the representation and recognition scheme to utilize multiple (polymorphic) types of primitive parts is presented and the approach is demonstrated on a selection of imagery.