Learning to form large groups of salient image features

We offer a novel strategy to adapt the perceptual organization process to an object and its contest in a scene. Given a set of training images of an object in context, a learning process decides on the relative importance of the basic Gestalt relationships such as proximity, parallelness, similarity, symmetry, closure, and common region towards segregating the object from the background. This learning is accomplished using a team of stochastic automata in a N-player cooperative game framework. The grouping process which is based on graph partitioning is able to form large groups from relationships defined over a small set of primitives and is fast. We demonstrate the robust performance of the growing system on a variety of real images. Among the interesting conclusions is the significant role of photometric attributes in grouping and the ability to perform figure-ground segmentation from a set of local relations, each defined over a small number of primitives.