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Optical Engineering

Evaluation of learning vector quantization to classify cotton trash
Author(s): Michael A. Lieberman; Rajendra B. Patil
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Paper Abstract

The cotton industry needs a method to identify the type of trash [nonlint material (NLM)] in cotton samples; learning vector quantization (LVQ) is evaluated as that method. LVQ is a classification technique that defines reference vectors (group prototypes) in an N-dimensional feature space (RN). Normalized trash object features extracted from images of compressed cotton samples define RN. An unknown NLM object is given the label of the closest reference vector (as defined by Euclidean distance). Different normalized feature spaces and NLM classifications are evaluated and accuracies reported for correctly identifying the NLM type. LVQ is used to partition cotton trash into: (1) bark (B), leaf (L), pepper (P), or stick (S); (2) bark and nonbark (N); or (3) bark, combined leaf and pepper (LP), or stick. Percentage accuracy for correctly identifying 139 pieces of test trash placed on laboratory prepared samples for the three scenarios are (B:95, L:87, P:100, S:88), (B:100, N:97), and (B:95, LP:99, S:88), respectively. Also, LVQ results are compared to previous work using backpropagating neural networks.

Paper Details

Date Published: 1 March 1997
PDF: 8 pages
Opt. Eng. 36(3) doi: 10.1117/1.601257
Published in: Optical Engineering Volume 36, Issue 3
Show Author Affiliations
Michael A. Lieberman, USDA Agricultural Research Service (United States)
Rajendra B. Patil, Los Alamos National Lab. (United States)


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