Two Layers Machine Learning Architecture for Animal Classification Using HOG and LBP

Abstract

There is a social relation between animals and humans. Both are directly or indirectly dependent on each other. Therefore, it is our duty to maintain the healthy existence of both. To maintain this situation, we have to develop an efficient model that is able to protect the life of animals. To do the same, first it is necessary to identify different animals so that we can concentrate more on the endangered species (animal). But due to various varieties of animals, we need a system to recognize such type of animals automatically. In this paper, we propose a system based on two-layered machine learning architecture by using histogram of oriented gradients (HOG) method and local binary pattern (LBP) to perform the detection of animal. SVM and gradient boosting classifiers are used on different layers to classify the object efficiently. The result analysis shows that our proposed model works efficiently and effectively with an acceptable accuracy, i.e., 95.15%.