Abstract
Zoonoses (Zoonotic diseases) transferred from animals to human leads to death of many people every year. Controlling and tracking infected animals may save millions of human’s life. One way to help achieve this is to develop an automatic animal identification/recognition systems. In this paper, a fully automated zebra animal identification approach is proposed. In this approach, the Scale Invariant Feature Transform (SIFT) feature extraction method is used to compute the features of 2D zebra images. A matching between training and testing images is calculated based on Support Vector Machine (SVM), Decision Tree (DT), and Fuzzy k-Nearest Neighbour (Fk-NN) classifiers. The experimental results show that the proposed approach is superior than other existed ones in terms of identification accuracy and the automation as our approach is fully automated while the other zebra identification systems are semi-automated or manual. The proposed approach achieved high recognition rate and the SVM classifier in this application is better than the other two classifiers.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Crall, J.P., Stewart, C.V., Berger-Wolf, T.Y., Rubenstein, D.I., Sundaresan, S.R.: Hotspotter-patterned species instance recognition. In: Proceedings of IEEE Workshop on Applications of Computer Vision (WACV), 230–237 (2013)
Tharwat, A., Gaber, T., Awad, Y.M., Dey, N., Hassanien, A.E.: Plants identification using feature fusion technique and bagging classifier. In: Gaber, T., Hassanien, A.E., El-Bendary, N., Dey, N. (eds.) The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt. AISC, vol. 407, pp. 461–471. Springer, Heidelberg (2016). doi:10.1007/978-3-319-26690-9_41
Ahmed, S., Gaber, T., Tharwat, A., Hassanien, A.E., Snáel, V.: Muzzle-based cattle identification using speed up robust feature approach. In: 2015 Proceedings of the International Conference on Intelligent Networking and Collaborative Systems (INCOS), pp. 99–104. IEEE (2015)
Tharwat, A., Gaber, T., Hassanien, A.E.: Two biometric approaches for cattle identification based on features and classifiers fusion. Int. J. Image Min. 1(4), 342–365 (2015)
Foster, G., Krijger, H., Bangay, S.: Zebra fingerprints: towards a computer-aided identification system for individual zebra. Afr. J. Ecol. 45(2), 225–227 (2007)
Lahiri, M., Tantipathananandh, C., Warungu, R., Rubenstein, D.I., Berger-Wolf, T.Y.: Biometric animal databases from field photographs: identification of individual zebra in the wild. In: Proceedings of the 1st ACM International Conference on Multimedia Retrieval, Article no. 6. ACM (2011)
Tharwat, A., Mahdi, H., El Hennawy, A., Hassanien, A.E.: Face sketch recognition using local invariant features. In: 2015 7th International Conference of Soft Computing and Pattern Recognition (SoCPaR), 117–122. IEEE (2015)
Semary, N.A., Tharwat, A., Elhariri, E., Hassanien, A.E.: Fruit-based tomato grading system using features fusion and support vector machine. In: Filev, D., Jabłkowski, J., Kacprzyk, J., Krawczak, M., Popchev, I., Rutkowski, L., Sgurev, V., Sotirova, E., Szynkarczyk, P., Zadrozny, S. (eds.) Intelligent Systems’2014. AISC, vol. 323, pp. 401–410. Springer, Heidelberg (2015). doi:10.1007/978-3-319-11310-4_35
Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms, 1st edn. Wiley, New York (2004)
Keller, J.M., Gray, M.R., Givens, J.A.: A fuzzy k-nearest neighbor algorithm. IEEE Trans. Syst. Man Cybern. 4, 580–585 (1985)
Gaber, T., Tharwat, A., Hassanien, A.E., Snasel, V.: Biometric cattle identification approach based on weber’s local descriptor and adaboost classifier. Comput. Electron. Agric. 122, 55–66 (2016)
Tharwat, A., Ghanem, A.M., Hassanien, A.E.: Three different classifiers for facial age estimation based on k-nearest neighbor. In: 2013 9th International Computer Engineering Conference (ICENCO), pp. 55–60. IEEE (2013)
Tharwat, A., Ibrahim, A., Ali, H.: Personal identification using ear images based on fast and accurate principal component analysis. In: 2012 Proceedings of 8th International Conference on Informatics and Systems (INFOS), pp. 56–59. IEEE (2012)
Tharwat, A., Ibrahim, A., Hassanien, A.E., Schaefer, G.: Ear recognition using block-based principal component analysis and decision fusion. In: Kryszkiewicz, M., Bandyopadhyay, S., Rybinski, H., Pal, S.K. (eds.) PReMI 2015. LNCS, vol. 9124, pp. 246–254. Springer, Heidelberg (2015). doi:10.1007/978-3-319-19941-2_24
Gaber, T., Tharwat, A., Snasel, V., Hassanien, A.E.: Plant identification: two dimensional-based vs. one dimensional-based feature extraction methods. In: Herrero, Á., Sedano, J., Baruque, B., Quintián, H., Corchado, E. (eds.) Premi 2015. AISC, vol. 368, pp. 375–385. Springer, Heidelberg (2015). doi:10.1007/978-3-319-19719-7_33
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Tharwat, A., Gaber, T., Hassanien, A.E., Schaefer, G., Pan, JS. (2017). A Fully-Automated Zebra Animal Identification Approach Based on SIFT Features. In: Pan, JS., Lin, JW., Wang, CH., Jiang, X. (eds) Genetic and Evolutionary Computing. ICGEC 2016. Advances in Intelligent Systems and Computing, vol 536. Springer, Cham. https://doi.org/10.1007/978-3-319-48490-7_34
Download citation
DOI: https://doi.org/10.1007/978-3-319-48490-7_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-48489-1
Online ISBN: 978-3-319-48490-7
eBook Packages: EngineeringEngineering (R0)