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Open AccessArticle

Bag-of-Visual-Words for Cattle Identification from Muzzle Print Images

by Ali Ismail Awad 1,2,3,* and M. Hassaballah 4
1
Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, 971 87 Luleå, Sweden
2
Faculty of Engineering, Al-Azhar University, P.O. Box 83513 Qena, Egypt
3
Centre for Security, Communications and Network Research, University of Plymouth, Plymouth PL4 8AA, UK
4
Department of Computer Science, Faculty of Computers and Information, South Valley University, P.O. Box 83523 Qena, Egypt
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(22), 4914; https://doi.org/10.3390/app9224914
Received: 7 September 2019 / Revised: 11 November 2019 / Accepted: 12 November 2019 / Published: 15 November 2019
(This article belongs to the Special Issue Deep Learning-Based Biometric Recognition)
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. View Full-Text
Keywords: computer vision; biometrics; cattle identification; bag-of-visual-words; muzzle print images computer vision; biometrics; cattle identification; bag-of-visual-words; muzzle print images
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Awad, A.I.; Hassaballah, M. Bag-of-Visual-Words for Cattle Identification from Muzzle Print Images. Appl. Sci. 2019, 9, 4914.

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