Generic model abstraction from examples
The recognition community has typically avoided bridging the representational gap between traditional, low- level image features and generic models. Instead, the gap has been artificially eliminated by either bringing the image closer to the models using simple scenes containing idealized, textureless objects or by bringing the models closer to the images using 3D CAD model templates or 2D appearance model templates. In this paper, we attempt to bridge the representational gap for the domain of model acquisition. Specifically, we address the problem of automatically acquiring a generic 2D view- based class model from a set of images, each containing an exemplar object belonging to that class. We introduce a novel graph- theoretical formulation of the problem in which we search for the lowest common abstraction among a set of lattices, each representing the space of all possible region groupings in a region adjacency graph representation of an input image. The problem is intractable and we present a shortest path- based approximation algorithm to yield an efficient solution. We demonstrate the approach on real imagery.
image abstraction automatic model acquisition learning from examples shape description object recognition graph algorithms