The animal classification: An evaluation of different transfer learning pipeline

• Main Authors: Ee, Ken-ji, Ahmad Fakhri, Ab. Nasir, Anwar P. P., Abdul Majeed, Mohd Azraai, Mohd Razman, Nur Hafieza, Ismail
• Format: Article
• Language: English
• Published: Penerbit UMP 2021
• Subjects: QA Mathematics; TJ Mechanical engineering and machinery; TS Manufactures
• Online Access: http://umpir.ump.edu.my/id/eprint/33646/1/The%20animal%20classification%20_%20an%20evaluation%20of%20different%20transfer.pdf; http://umpir.ump.edu.my/id/eprint/33646/; https://doi.org/10.15282/mekatronika.v3i1.6680; https://doi.org/10.15282/mekatronika.v3i1.6680
• Institution: Universiti Malaysia Pahang

The animal classification system is a technology to classify the animal class (type) automatically and useful in many applications. There are many types of learning models applied to this technology recently. Nonetheless, it is worth noting that the extraction of the features and the classification of the animal features is non-trivial, particularly in the deep learning approach for a successful animal classification system. The use of Transfer Learning (TL) has been demonstrated to be a powerful tool in the extraction of essential features. However, the employment of such a method towards animal classification applications are somewhat limited. The present study aims to determine a suitable TL-conventional classifier pipeline for animal classification. The VGG16 and VGG19 were used in extracting features and then coupled with either k-Nearest Neighbour (k-NN) or Support Vector Machine (SVM) classifier. Prior to that, a total of 4000 images were gathered consisting of a total of five classes which are cows, goats, buffalos, dogs, and cats. The data was split into the ratio of 80:20 for train and test. The classifiers hyper parameters are tuned by the Grids Search approach that utilises the five-fold cross-validation technique. It was demonstrated from the study that the best TL pipeline identified is the VGG16 along with an optimised SVM, as it was able to yield an average classification accuracy of 0.975. The findings of the present investigation could facilitate animal classification application, i.e. for monitoring animals in wildlife.