Author(s): Matthew O. Ward, Computer Science Department, Worcester Polytechnic Institute, Worcester, MA 01609, and Wai Ping Lam, AT&T Bell Laboratories, 79 Midhurst Court, 201C, Naperville, IL 60566

Source: Proc. of SPIE Symposium on Advances in Intelligent Robotics Systems, 1989.

Abstract: It is conjectured that humans learn to recognize objects by incrementally modifying and extending an internal representation based on the characteristics which distinguish objects from the rest of the environment. As new objects are encountered, it is often required to recall similar yet distinct objects and determine what differentiates the new objects from the old. Sometimes all that is required is to refine the allowable range for a particular feature, i.e. use a higher level of precision. Other times a previously useful feature must be discarded for a more powerful one in order to perform efficient recognition. This paper describes a system which has been developed based on this conjecture. Initially, a decision tree is created for recognizing a set of training objects using an automatically selected subset of extractable features. Factors involved in the creation include the cost of extraction and comparison of features, their discriminating strength within the domain, and the stability or separability of classes of objects using the features. The system then allows incremental, local modification of the tree to accommodate new objects or instances of old objects which were incorrectly identified. Various strategies for tree modification have been implemented, some of which guarantee the correct recognition of objects previously recognized and others which require some degree of retraining to maintain perfect recollection. Strategy selection is based on the technique which minimizes a metric based on the increased cost and complexity of the tree and the potential decrease in the stability of recognition.