Big Cats Classification Based on Body Covering

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 anima...

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Autores principales:	Fernanda Januar Pratama, Wikky Fawwaz Al Maki, Febryanti Sthevanie
Formato:	article
Lenguaje:	ID
Publicado:	Ikatan Ahli Indormatika Indonesia 2021
Materias:	clahe image processing median filter support vector machine pyramid histogram of oriented gradients Systems engineering TA168 Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/10a00bc38cb3447ca8a0df82ad7807fe
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id	oai:doaj.org-article:10a00bc38cb3447ca8a0df82ad7807fe
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spelling	oai:doaj.org-article:10a00bc38cb3447ca8a0df82ad7807fe2021-11-16T13:16:11ZBig Cats Classification Based on Body Covering2580-076010.29207/resti.v5i5.3328https://doaj.org/article/10a00bc38cb3447ca8a0df82ad7807fe2021-10-01T00:00:00Zhttp://jurnal.iaii.or.id/index.php/RESTI/article/view/3328https://doaj.org/toc/2580-0760The 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%.Fernanda Januar PratamaWikky Fawwaz Al MakiFebryanti SthevanieIkatan Ahli Indormatika Indonesiaarticleclaheimage processingmedian filtersupport vector machinepyramid histogram of oriented gradientsSystems engineeringTA168Information technologyT58.5-58.64IDJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), Vol 5, Iss 5, Pp 984-991 (2021)
institution	DOAJ
collection	DOAJ
language	ID
topic	clahe image processing median filter support vector machine pyramid histogram of oriented gradients Systems engineering TA168 Information technology T58.5-58.64
spellingShingle	clahe image processing median filter support vector machine pyramid histogram of oriented gradients Systems engineering TA168 Information technology T58.5-58.64 Fernanda Januar Pratama Wikky Fawwaz Al Maki Febryanti Sthevanie Big Cats Classification Based on Body Covering
description	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%.
format	article
author	Fernanda Januar Pratama Wikky Fawwaz Al Maki Febryanti Sthevanie
author_facet	Fernanda Januar Pratama Wikky Fawwaz Al Maki Febryanti Sthevanie
author_sort	Fernanda Januar Pratama
title	Big Cats Classification Based on Body Covering
title_short	Big Cats Classification Based on Body Covering
title_full	Big Cats Classification Based on Body Covering
title_fullStr	Big Cats Classification Based on Body Covering
title_full_unstemmed	Big Cats Classification Based on Body Covering
title_sort	big cats classification based on body covering
publisher	Ikatan Ahli Indormatika Indonesia
publishDate	2021
url	https://doaj.org/article/10a00bc38cb3447ca8a0df82ad7807fe
work_keys_str_mv	AT fernandajanuarpratama bigcatsclassificationbasedonbodycovering AT wikkyfawwazalmaki bigcatsclassificationbasedonbodycovering AT febryantisthevanie bigcatsclassificationbasedonbodycovering
_version_	1718426465176911872

Big Cats Classification Based on Body Covering

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