Color-based butterfly species identification using Support Vector Machine / Norina Arliana Sopiana

Previously, few of expert found that the butterfly identification was complicated, more time consuming and difficult. Butterfly species are difficult to identify because they have different in size, color, and shape. Butterfly Species Identification is an identification of butterfly species that inv...

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Main Author:	Norina Arliana Sopiana
Format:	Thesis
Language:	English
Published:	2017
Online Access:	https://ir.uitm.edu.my/id/eprint/18225/2/TD_NORINA%20ARLIANA%20SOPIANA%20CS%2017_5.pdf
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id	my-uitm-ir.18225
record_format	uketd_dc
spelling	my-uitm-ir.182252019-05-23T07:55:56Z Color-based butterfly species identification using Support Vector Machine / Norina Arliana Sopiana 2017 Norina Arliana Sopiana Previously, few of expert found that the butterfly identification was complicated, more time consuming and difficult. Butterfly species are difficult to identify because they have different in size, color, and shape. Butterfly Species Identification is an identification of butterfly species that involves a butterfly image. The purpose of this project is to identify butterfly species. This project proposed an appropriate method for the butterfly species identification which is to identify the species of the butterfly images. The scope of this project covers of two species of butterfly which are Vanessa atalanta and Aglais io. The data has been collected from Datasets for Computer Vision Research website. For project methodologies, there are three phases such as data collection, processing and post-processing. For data collection, two species are involved Vanessa atalanta and Aglais io that have 100 data image for each species. In processing phase, there have feature extraction and butterfly species identification. For feature extraction, color histogram technique was used to extract feature from butterfly image. The features include mean, variance, standard deviation and skewness. These color features are calculated from the color component of red, green and blue. This feature classify using Support Vector Machine (SVM) was applied as a technique for identify two group of butterfly species. Testing and evaluation are includes in postprocessing phase. The results proved that it significantly works on two butterfly species of Vanessa atalanta and Aglais io to classify that butterfly images. Therefore, this prototype will be significantly benefits to the users. 2017 Thesis https://ir.uitm.edu.my/id/eprint/18225/ https://ir.uitm.edu.my/id/eprint/18225/2/TD_NORINA%20ARLIANA%20SOPIANA%20CS%2017_5.pdf text en public dphil degree Universiti Teknologi MARA Faculty of Computer and Mathematical Sciences
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
description	Previously, few of expert found that the butterfly identification was complicated, more time consuming and difficult. Butterfly species are difficult to identify because they have different in size, color, and shape. Butterfly Species Identification is an identification of butterfly species that involves a butterfly image. The purpose of this project is to identify butterfly species. This project proposed an appropriate method for the butterfly species identification which is to identify the species of the butterfly images. The scope of this project covers of two species of butterfly which are Vanessa atalanta and Aglais io. The data has been collected from Datasets for Computer Vision Research website. For project methodologies, there are three phases such as data collection, processing and post-processing. For data collection, two species are involved Vanessa atalanta and Aglais io that have 100 data image for each species. In processing phase, there have feature extraction and butterfly species identification. For feature extraction, color histogram technique was used to extract feature from butterfly image. The features include mean, variance, standard deviation and skewness. These color features are calculated from the color component of red, green and blue. This feature classify using Support Vector Machine (SVM) was applied as a technique for identify two group of butterfly species. Testing and evaluation are includes in postprocessing phase. The results proved that it significantly works on two butterfly species of Vanessa atalanta and Aglais io to classify that butterfly images. Therefore, this prototype will be significantly benefits to the users.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Bachelor degree
author	Norina Arliana Sopiana
spellingShingle	Norina Arliana Sopiana Color-based butterfly species identification using Support Vector Machine / Norina Arliana Sopiana
author_facet	Norina Arliana Sopiana
author_sort	Norina Arliana Sopiana
title	Color-based butterfly species identification using Support Vector Machine / Norina Arliana Sopiana
title_short	Color-based butterfly species identification using Support Vector Machine / Norina Arliana Sopiana
title_full	Color-based butterfly species identification using Support Vector Machine / Norina Arliana Sopiana
title_fullStr	Color-based butterfly species identification using Support Vector Machine / Norina Arliana Sopiana
title_full_unstemmed	Color-based butterfly species identification using Support Vector Machine / Norina Arliana Sopiana
title_sort	color-based butterfly species identification using support vector machine / norina arliana sopiana
granting_institution	Universiti Teknologi MARA
granting_department	Faculty of Computer and Mathematical Sciences
publishDate	2017
url	https://ir.uitm.edu.my/id/eprint/18225/2/TD_NORINA%20ARLIANA%20SOPIANA%20CS%2017_5.pdf
_version_	1783733637776146432

Color-based butterfly species identification using Support Vector Machine / Norina Arliana Sopiana

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