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 multiple scales using a wavelet transform. This compact representation is highly invariant to translation, rotation (image and depth), and scaling, and offers the locality of representation required for occluded object recognition. 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 geometrical 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.
view-based object recognition shape representation and recovery graph matching