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

Ali Ismail Awad,M Hassaballah

doi:10.3390/app9224914

Abstract

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.

Highlights

Cattle, buffalo and cows are the major sources of meat in the food supply chain and their protection has become a vital need
A robust and accurate cattle identification mechanism is vital for protecting livestock, limiting livestock producers’ losses to disease and facilitating cattle ownership management
This paper has explored the performance of the BoVW paradigm in cattle identification using speeded-up robust features (SURF) and maximally stable extremal regions (MSER) as engines for BoVW

Summary

Introduction

Buffalo and cows are the major sources of meat in the food supply chain and their protection has become a vital need. Cattle identification is beneficial to different stakeholders, including animal producers, food consumers and the food industry [1]. Cattle identification systems contribute to limiting the spread of animal diseases by allowing a better understanding of disease trajectories and effectively managing cattle vaccination programs. Cattle identification helps in limiting cattle losses, reducing the costs of disease destruction, minimizing trade losses and facilitating cattle ownership management in large-scale farms [2,3]. Conventional buffalo and cow identification methods are divided into three groups—permanent, temporary and electrical identification methods [4].

Results

Discussion

Conclusion