Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, Cambridge, England, 23 - 26 August 2004, vol.2, pp.273-276
Given a collection of sets of 2-D views of 3-D objects and a similarity measure between them, we present a method for summarizing the sets using a small subset called a bounded canonical set (BCS), whose members best represent the members of the original set. This means that members of the BCS are as dissimilar from each other as possible, while at the same time being as similar as possible to the non-BCS members. This paper will extend our earlier work on computing canonical sets in several ways: by omitting the need for a multi-objective optimization, by allowing the imposition of cardinality constraints, and by introducing a total similarity function. We evaluate the applicability of BCS to view selection in a view-based object recognition environment.