Species–identification of wasps using principal component associative memories

This paper presents a novel approach to image-based insect specimen identification, exploiting the ability of principal component auto associative memories to form trainable classifiers, which may be used to identify unknown images. The system utilises the differences between a pair of reconstructed...

Full description

Saved in:
Bibliographic Details
Published in:Image and vision computing 1999, Vol.17 (12), p.861-866
Main Authors: Weeks, P.J.D, O'Neill, M.A, Gaston, K.J, Gauld, I.D
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Computer science
control theory
systems
Exact sciences and technology
Mathematics
Numerical analysis
Numerical analysis. Scientific computation
Pattern recognition. Digital image processing. Computational geometry
Principal components analysis
Sciences and techniques of general use
Species-identification
Taxonomy
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper presents a novel approach to image-based insect specimen identification, exploiting the ability of principal component auto associative memories to form trainable classifiers, which may be used to identify unknown images. The system utilises the differences between a pair of reconstructed images produced when the unknown image is included in, and then excluded from the training set encoded by the auto associative memory. A non-parametric statistical correlation metric, Kendall's, was used to correlate the reconstructed images. The approach has been applied to the species-identification of closely related parasitic wasps based upon their wing venation and pigmentation patterns.
ISSN:0262-8856
1872-8138
DOI:10.1016/S0262-8856(98)00161-9