Open AccessBig Cats Classification Based on Body Covering
Author(s) -
Fernanda Januar Pratama,
Wikky Fawwaz Al Maki,
Febryanti Sthevanie
Publication year - 2021
Publication title -
jurnal resti (rekayasa sistem dan teknologi informasi)
Language(s) - English
Resource type - Journals
ISSN - 2580-0760
DOI - 10.29207/resti.v5i5.3328
Subject(s) - artificial intelligence , support vector machine , pattern recognition (psychology) , grayscale , computer science , histogram , classifier (uml) , feature extraction , segmentation , population , image processing , image segmentation , pyramid (geometry) , contextual image classification , computer vision , image (mathematics) , mathematics , demography , geometry , sociology
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%.