Interactive learning of multiple attribute hash table for fast 3D object recognition

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 hash tables must be created by the system developer-an onerous task especially when the number of attributes is large, which is the case in systems that use both geometric and nongeometric attributes. The authors show how the tools of decision trees can be used for the automatic construction of hash tables. Their decision tree framework 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. In the system proposed the system developer shows objects to a 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