Big Cats Classification Based on Body Covering

Big Cats Classification Based on Body Covering

CLAHE, Segmentation, PHOG, Support Vector Machines

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 p

Fernanda Januar Pratama1
, Wikky Fawwaz Al Maki2
, Febryanti Sthevanie3

Telkom University

31-10-2021

Fajar bagus W

PDF

Indonesia

Text