Learning 3D Object Recognition Models from 2D Images

Abstract

To recognize an object in an image one must have some internal model of how that object may appear. We show how to learn such a model from a series of training images depicting a class of objects. The model represents a 3D object by a set of characteristic views, each defining a probability distribution over variation in object appearance. Features identified in an image through perceptual organization are represented by a graph whose nodes include feature labels and numeric measurements. Image graphs are partitioned into characteristic views by an incremental conceptual clustering algorithm. A learning procedure generalizes multiple image graphs to form a characteristic view graph in which the numeric measurements are described by probability distributions. A matching procedure, using a similarity metric based on a non–parametric probability density estimator, compares image and characteristic view graphs to identify an instance of a modeled object in an image. We present experimental results from a system constructed to test this approach. The system is demonstrated learning to recognize partially occluded objects in images using shape cues.