Learning object models from graph templates

Abstract

This paper describes a novel neural network (NN) based system for detecting rigid objects from their (2-D) gray-level image images. In this approach a labeled graph is employed to construct a template model for an object from the image region where the object is located. A novel network of NN (NoNN) is proposed to learn the examples of object model graph templates. The NoNN is composed of a set of subnetworks that are not connected to one another. The selected network topology improves the generalization of the classifier in terms of its Vapnik-Chervonenkis dimension (VCdim). Each subnetwork is a network of multilayer perceptron neural network classifiers operating in parallel with the rest of the system. Each subnetwork is assigned to learn the label of one vertex of a graph. The detection scheme combines the decisions of the subnetworks to classify an image graph extracted from an input image block. This visual computational model is potentially useful for partial matching where the object is occluded. Performance of the system is tested in modeling and detection of human eye regions in face images with some degree of variation in the direction of pose.