TY - JOUR
T1 - Bag-of-visual-words for cattle identification from muzzle print images
AU - Awad, Ali Ismail
AU - Hassaballah, M.
N1 - Funding Information:
The authors would like to express their gratitude to A. E. Hassanien and team of Scientific Research Group in Egypt (SRGE), Cairo University, Egypt, for providing us with the cattle muzzle print image database used in this study. This research received no external funding.
Publisher Copyright:
© 2019 by the authors.
PY - 2019/11/1
Y1 - 2019/11/1
N2 - Cattle, buffalo and cow identification plays an influential role in cattle traceability from birth to slaughter, understanding disease trajectories and large-scale cattle ownership management. Muzzle print images are considered discriminating cattle biometric identifiers for biometric-based cattle identification and traceability. This paper presents an exploration of the performance of the bag-of-visual-words (BoVW) approach in cattle identification using local invariant features extracted from a database of muzzle print images. Two local invariant feature detectors-namely, speeded-up robust features (SURF) and maximally stable extremal regions (MSER)-are used as feature extraction engines in the BoVW model. The performance evaluation criteria include several factors, namely, the identification accuracy, processing time and the number of features. The experimental work measures the performance of the BoVW model under a variable number of input muzzle print images in the training, validation, and testing phases. The identification accuracy values when utilizing the SURF feature detector and descriptor were 75%, 83%, 91%, and 93% for when 30%, 45%, 60%, and 75% of the database was used in the training phase, respectively. However, using MSER as a points-of-interest detector combined with the SURF descriptor achieved accuracies of 52%, 60%, 67%, and 67%, respectively, when applying the same training sizes. The research findings have proven the feasibility of deploying the BoVW paradigm in cattle identification using local invariant features extracted from muzzle print images.
AB - Cattle, buffalo and cow identification plays an influential role in cattle traceability from birth to slaughter, understanding disease trajectories and large-scale cattle ownership management. Muzzle print images are considered discriminating cattle biometric identifiers for biometric-based cattle identification and traceability. This paper presents an exploration of the performance of the bag-of-visual-words (BoVW) approach in cattle identification using local invariant features extracted from a database of muzzle print images. Two local invariant feature detectors-namely, speeded-up robust features (SURF) and maximally stable extremal regions (MSER)-are used as feature extraction engines in the BoVW model. The performance evaluation criteria include several factors, namely, the identification accuracy, processing time and the number of features. The experimental work measures the performance of the BoVW model under a variable number of input muzzle print images in the training, validation, and testing phases. The identification accuracy values when utilizing the SURF feature detector and descriptor were 75%, 83%, 91%, and 93% for when 30%, 45%, 60%, and 75% of the database was used in the training phase, respectively. However, using MSER as a points-of-interest detector combined with the SURF descriptor achieved accuracies of 52%, 60%, 67%, and 67%, respectively, when applying the same training sizes. The research findings have proven the feasibility of deploying the BoVW paradigm in cattle identification using local invariant features extracted from muzzle print images.
KW - Bag-of-visual-words
KW - Biometrics
KW - Cattle identification
KW - Computer vision
KW - Muzzle print images
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U2 - 10.3390/app9224914
DO - 10.3390/app9224914
M3 - Article
AN - SCOPUS:85075233197
SN - 2076-3417
VL - 9
JO - Applied Sciences (Switzerland)
JF - Applied Sciences (Switzerland)
IS - 22
M1 - 4914
ER -