Big Cats Classification Based on Body Covering

Fernanda Januar Pratama Fernanda Januar Pratama,Febryanti Sthevanie Febryanti Sthevanie,Wikky Fawwaz Al Maki Wikky Fawwaz Al Maki

doi:10.29207/resti.v5i5.3328

Fernanda Januar Pratama Fernanda Januar Pratama, Febryanti Sthevanie Febryanti Sthevanie + Show 1 more

Open Access

https://doi.org/10.29207/resti.v5i5.3328

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Abstract

The reduced habitat owned by an animal has a very bad impact on the survival of the animal, resulting in a continuous decrease in the number of animal populations especially in animals belonging to the big cat family such as tigers, cheetahs, jaguars, and others. To overcome the decline in the animal population, a classification model was built to classify images that focuses on the pattern of body covering possessed by animals. However, in designing an accurate classification model with an optimal level of accuracy, it is necessary to consider many aspects such as the dataset used, the number of parameters, and computation time. In this study, we propose an animal image classification model that focuses on animal body covering by combining the Pyramid Histogram of Oriented Gradient (PHOG) as the feature extraction method and the Support Vector Machine (SVM) as the classifier. Initially, the input image is processed to take the body covering pattern of the animal and converted it into a grayscale image. Then, the image is segmented by employing the median filter and the Otsu method. Therefore, the noise contained in the image can be removed and the image can be segmented. The results of the segmentation image are then extracted by using the PHOG and then proceed with the classification process by implementing the SVM. The experimental results showed that the classification model has an accuracy of 91.07%.

Highlights

The reduced habitat owned by an animal has a very bad impact on the survival of the animal, resulting in a continuous decrease in the number of animal populations especially in animals belonging to the big cat family such as tigers, cheetahs, jaguars, and others
All images are converted into vectorcaused by humans causes many wild animals to lose based representations and the manifold learning their homes, overfishing and illegal hunting are factors algorithm is employed to reduce the dimensions of the that accelerate the decline in the animal population
Experimental results image processing and taking advantage of the showed that four classification methods employed in the increasingly high use of digital cameras, this problem study, i.e., logistic regression, KNN, Support Vector Machine (SVM), and random can be solved by developing a classification model that forest achieved different accuracy rates, i.e., 98%, 97%, can classify the image obtained from digital cameras

Summary

Introduction

The reduced habitat owned by an animal has a very bad impact on the survival of the animal, resulting in a continuous decrease in the number of animal populations especially in animals belonging to the big cat family such as tigers, cheetahs, jaguars, and others. We propose an animal image classification model that focuses on animal body covering by combining the Pyramid Histogram of Oriented Gradient (PHOG) as the feature extraction method and the Support Vector Machine (SVM) as the classifier. Experimental results image processing and taking advantage of the showed that four classification methods employed in the increasingly high use of digital cameras, this problem study, i.e., logistic regression, KNN, SVM, and random can be solved by developing a classification model that forest achieved different accuracy rates, i.e., 98%, 97%, can classify the image obtained from digital cameras. The classification method on images has many obstacles, such as long computational time due to large data sets and the use of high-resolution sensors, image capture involving animals with complex backgrounds, different postures, and lighting [1].

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