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Proceedings Paper

Fast-search nearest neighbor classification based on structured templates
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Paper Abstract

A mine detection algorithm based on the us of structured templates applied to acoustic backscatter data is proposed. The structured templates correspond to the codevectors of a type of cluster-based compression algorithm called residual vector quantization (RVQ). The RVQ clusters have a hierarchical structure that permits efficient searches for nearest neighbor templates, and efficient dictionary storage for memory cost reduction. The structured templates are generated by a multistage synthesis process that produces a sequence of finite precision representations of training data. This successive approximation process is combined with a sequential classification process to form a new type of classifier called a direct sum successive approximation classifier.

Paper Details

Date Published: 22 July 1997
PDF: 11 pages
Proc. SPIE 3079, Detection and Remediation Technologies for Mines and Minelike Targets II, (22 July 1997);
Show Author Affiliations
Christopher F. Barnes, Georgia Tech Research Institute (United States)

Published in SPIE Proceedings Vol. 3079:
Detection and Remediation Technologies for Mines and Minelike Targets II
Abinash C. Dubey; Robert L. Barnard, Editor(s)

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