Biometric cattle identification approach based on Weber's Local Descriptor and AdaBoost classifier
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F16%3A86099070" target="_blank" >RIV/61989100:27240/16:86099070 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1016/j.compag.2015.12.022" target="_blank" >http://dx.doi.org/10.1016/j.compag.2015.12.022</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.compag.2015.12.022" target="_blank" >10.1016/j.compag.2015.12.022</a>
Alternative languages
Result language
angličtina
Original language name
Biometric cattle identification approach based on Weber's Local Descriptor and AdaBoost classifier
Original language description
In this paper, we proposed a new and robust biometric-based approach to identify head of cattle. This approach used the Weber Local Descriptor (WLD) to extract robust features from cattle muzzle print images (images from 31 head of cattle were used). It also employed the AdaBoost classifier to identify head of cattle from their WLD features. To validate the results obtained by this classifier, other two classifiers (k-Nearest Neighbor (k-NN) and Fuzzy- k-Nearest Neighbor (F. k-NN)) were used. The experimental results showed that the proposed approach achieved a promising accuracy result (approximately 99.5%) which is better than existed proposed solutions. Moreover, to evaluate the results of the proposed approach, four different assessment methods (Area Under Curve (AUC), Sensitivity and Specificity, accuracy rate, and Equal Error Rate (EER)) were used. The results of all these methods showed that the WLD along with AdaBoost algorithm gave very promising results compared to both of the k-NN and F. k-NN algorithms. (C) 2016 Elsevier B.V..
Czech name
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Czech description
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Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Computers and Electronics in Agriculture
ISSN
0168-1699
e-ISSN
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Volume of the periodical
122
Issue of the periodical within the volume
MAR
Country of publishing house
US - UNITED STATES
Number of pages
12
Pages from-to
55-66
UT code for WoS article
000371944900006
EID of the result in the Scopus database
2-s2.0-84955305738