Interactive Learning of a Multiple-Attribute Hash Table Classifier for Fast Object Recognition

Abstract

Multiple-attribute hashing is now considered to be a powerful approach for the recognition and localization of 3D objects on the basis of their invariant properties. In the systems developed to date, the structure of the hash table is fixed and must be created by the system developer—an onerous task especially when the number of attributes is large, as it must in systems that use both geometric and nongeometric attributes. Another deficiency of previous systems is that uncertainty is treated as a fixed value and not modeled. In this paper, we will present a system, named MULTI-HASH, which uses the tools of decision trees and uncertainty modeling for the automatic construction of hash tables. The decision-tree framework in MULTI-HASH is based on a hybrid method that uses both qualitative attributes, such as the shape of a surface, and quantitative attributes such as color, dihedral angles, etc. The human trainer shows objects to the vision system and, in an interactive mode, tells the system the model identities of the various segmented regions, etc. Subsequently, the decision-tree-based framework learns the structure of the hash table.