1 March 1997 Evaluation of learning vector quantization to classify cotton trash
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Optical Engineering, 36(3), (1997). doi:10.1117/1.601257
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.
Michael A. Lieberman, Rajendra B. Patil, "Evaluation of learning vector quantization to classify cotton trash," Optical Engineering 36(3), (1 March 1997). http://dx.doi.org/10.1117/1.601257
JOURNAL ARTICLE
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KEYWORDS
Quantization

Cameras

Image segmentation

Optical engineering

Neural networks

Brain

CCD cameras

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