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New colour SIFT descriptors for image classification with applications to biometrics

New colour SIFT descriptors for image classification with applications to biometrics This paper first presents a new oRGB-SIFT descriptor, and then integrates it with other colour SIFT features to produce the novel Colour SIFT Fusion (CSF) and the Colour Greyscale SIFT Fusion (CGSF) descriptors for image classification with special applications to biometrics. Classification is implemented using a novel EFM-KNN classifier, which combines the Enhanced Fisher Model (EFM) and the K Nearest Neighbour (KNN) decision rule. The effectiveness of the proposed descriptors and classification method are evaluated using 20 image categories from two large scale, grand challenge datasets: the Caltech 256 database and the UPOL Iris database. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Biometrics Inderscience Publishers

New colour SIFT descriptors for image classification with applications to biometrics

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd. All rights reserved
ISSN
1755-8301
eISSN
1755-831X
DOI
10.1504/IJBM.2011.037714
Publisher site
See Article on Publisher Site

Abstract

This paper first presents a new oRGB-SIFT descriptor, and then integrates it with other colour SIFT features to produce the novel Colour SIFT Fusion (CSF) and the Colour Greyscale SIFT Fusion (CGSF) descriptors for image classification with special applications to biometrics. Classification is implemented using a novel EFM-KNN classifier, which combines the Enhanced Fisher Model (EFM) and the K Nearest Neighbour (KNN) decision rule. The effectiveness of the proposed descriptors and classification method are evaluated using 20 image categories from two large scale, grand challenge datasets: the Caltech 256 database and the UPOL Iris database.

Journal

International Journal of BiometricsInderscience Publishers

Published: Jan 1, 2011

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