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Proceedings Paper

A Binary Tree Classifier for Ship Targets
Author(s): B. A. Parvin; B. H. Yin; R. J. Hickman; R. D. Holben
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Paper Abstract

This paper describes the design of a binary tree classifier for ship targets. The design methodology is general enough so that it can be utilized for other classification problems. A hierarchical clustering procedure is employed to i) discover the underlying structure of data, and ii) construct the binary tree skeleton. The best feature subset, at each nonterminal node of the tree skeleton, is selected through a multivariate stepwise procedure which attempts to maximize the class separability. Further, this stepwise approach continues, until the probability of error at each nonterminal node with respect to a quadratic discriminant function is minimized. The proposed tree classifier has been evaluated against 1300 samples and classification accuracy of 85% versus 62% for the single stage classifier is achieved.

Paper Details

Date Published: 9 January 1984
PDF: 8 pages
Proc. SPIE 0432, Applications of Digital Image Processing VI, (9 January 1984); doi: 10.1117/12.936656
Show Author Affiliations
B. A. Parvin, Ford Aerospace and Communications Corporation (United States)
B. H. Yin, Ford Aerospace and Communications Corporation (United States)
R. J. Hickman, Ford Aerospace and Communications Corporation (United States)
R. D. Holben, Ford Aerospace and Communications Corporation (United States)


Published in SPIE Proceedings Vol. 0432:
Applications of Digital Image Processing VI
Andrew G. Tescher, Editor(s)

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