The combinatorics of heuristic search termination for object recognition in cluttered environments

Many current recognition systems terminate a search once an interpretation that is good enough is found. The author formally examines the combinatorics of this approach, showing that choosing correct termination procedures can dramatically reduce the search. In particular, the author provides conditions on the object model and the scene clutter such that the expected search is at most quartic. The analytic results are shown to be in agreement with empirical data for cluttered object recognition. These results imply that it is critical to use techniques that select subsets of the data likely to have come from a single object before establishing a correspondence between data and model features