Biometric cattle identification approach based on Weber's Local Descriptor and AdaBoost classifier

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Biometric cattle identification approach based on Weber's Local Descriptor and AdaBoost classifier

Popis výsledku v původním jazyce

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..

Název v anglickém jazyce

Biometric cattle identification approach based on Weber's Local Descriptor and AdaBoost classifier

Popis výsledku anglicky

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..

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Computers and Electronics in Agriculture

ISSN

0168-1699

e-ISSN

—

Svazek periodika

122

Číslo periodika v rámci svazku

MAR

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

12

Strana od-do

55-66

Kód UT WoS článku

000371944900006

EID výsledku v databázi Scopus

2-s2.0-84955305738