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.
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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
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DOI: https://doi.org/10.1007/978-3-319-48490-7_34
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