View-based object recognition using saliency maps

We introduce a novel view-based object representation, called the saliency map graph (SMG), which captures the salient regions of an object view at mu ltiple scales using a wavelet transform. This compact representation is hig hly invariant to translation, rotation (image and depth), and scaling, and offers the locality of representation required for occluded object recognit ion. To compare two saliency map graphs, we introduce two graph similarity algorithms. The first computes the topological similarity between two SMGs, providing a coarse-level matching of two graphs. The second computes the g eometrical similarity between two SMGs, providing a fine-level matching of two graphs. We test and compare these two algorithms on a large database of model object views. (C) 1999 Elsevier Science B.V. All rights reserved.