Object Recognition Using Subspace Methods

In this paper we describe a new recognition method that uses a subspace representation to approximate the comparison of binary images (e.g. intensity edges) using the Hausdorff fraction. The technique is robust to outliers and occlusion, and thus can be used for recognizing objects that are partly hidden from view and occur in cluttered backgrounds. We report some simple recognition experiments in which novel views of objects are classified using both a standard SSD-based eigenspace method and our Hausdorff-based method. These experiments illustrate how our method performs better when the background is unknown or the object is partially occluded. We then consider incorporating the method into an image search engine, for locating instances of objects under translation in an image. Results indicate that all but a small percentage of image locations can be ruled out using the eigenspace, without eliminating correct matches. This enables an image to be searched efficiently for any of the objects in an image database.