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Proceedings Paper

Object Recognition with Adaptive Decision Trees
Author(s): Matthew O. Ward; Wai Ping Lam
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Paper 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.

Paper Details

Date Published: 1 March 1990
PDF: 13 pages
Proc. SPIE 1192, Intelligent Robots and Computer Vision VIII: Algorithms and Techniques, (1 March 1990); doi: 10.1117/12.969786
Show Author Affiliations
Matthew O. Ward, Worcester Polytechnic Institute (United States)
Wai Ping Lam, AT&T Bell Laboratories (United States)


Published in SPIE Proceedings Vol. 1192:
Intelligent Robots and Computer Vision VIII: Algorithms and Techniques
David P. Casasent, Editor(s)

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