Learning object recognition models from images

To recognize an object in an image an internal model is required to indicate how that object may appear. The authors show how to learn such a model from a series of training images depicting a class of objects, producing a model that represents a probability distribution over the 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. A learning procedure generalizes multiple image graphs to form a model graph in which the numeric measurements are characterized by probability distributions. A matching procedure, using a similarity metric based on a non-parametric probability density estimator, compares model and image graphs to identify an instance of a modeled object in an image. Experimental results are presented from a system constructed to test this approach. The system learns to recognize partially occluded 2-D objects in 2-D images using shape cues. It can recognize objects as similar in general appearance while distinguishing them by their detailed features