Fast-search nearest neighbor classification based on structured templates

Christopher F. Barnes

doi:10.1117/12.280904

22 July 1997 Fast-search nearest neighbor classification based on structured templates

Christopher F. Barnes

Proceedings Volume 3079, Detection and Remediation Technologies for Mines and Minelike Targets II; (1997) https://doi.org/10.1117/12.280904
Event: AeroSense '97, 1997, Orlando, FL, United States

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.

Citation Download Citation

Christopher F. Barnes "Fast-search nearest neighbor classification based on structured templates", Proc. SPIE 3079, Detection and Remediation Technologies for Mines and Minelike Targets II, (22 July 1997); https://doi.org/10.1117/12.280904

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