Feature discovery in gray level imagery for one-class object recognition

摘要

Feature extraction transforms an object's image representation to an alternate reduced representation. In one-class object recognition, we would like this alternate representation to give improved discrimination between the object and all possible non-objects and improved generation between different object poses. Feature selection can be time-consuming and difficult to optimize so we have investigated unsupervised neural networks for feature discovery. We first discuss an inherent limitation in competitive type neural networks for discovering features in gray level images. We then show how Sanger's Generalized Hebbian Algorithm (GHA) removes this limitation and describe a novel GHA application for learning object features that discriminate the object from clutter. Using a specific example, we show how these features are better at distinguishing the target object from other nontarget object with Carpenter's ART 2-A as the pattern classifier.