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Leaf Classification using Local Binary Pattern and Histogram of Oriented Gradients

Computer vision; Graphic methods; Learning systems; Support vector machines; Classification process; Complex structure; Feature extractor; Histogram of oriented gradients; Leaf classification; Local binary patterns; Medical treatment; Oriented gradients; Classification (of information)

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Bibliographic Details
Main Authors: Janahiraman T.V., Yee L.K., Der C.S., Aris H.
Other Authors: 35198314400
Format: Conference Paper
Published: Institute of Electrical and Electronics Engineers Inc. 2023
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id my.uniten.dspace-24625
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spelling my.uniten.dspace-246252023-05-29T15:25:15Z Leaf Classification using Local Binary Pattern and Histogram of Oriented Gradients Janahiraman T.V. Yee L.K. Der C.S. Aris H. 35198314400 57215363797 57215357674 13608397500 Computer vision; Graphic methods; Learning systems; Support vector machines; Classification process; Complex structure; Feature extractor; Histogram of oriented gradients; Leaf classification; Local binary patterns; Medical treatment; Oriented gradients; Classification (of information) Categorization of plant species is a significant process in studying the diversity of different plant species in order to utilize it as medical treatment and to keep track of invasive plant species to maintain the balance of the environment. However, plants have extremely complex structure and diverse with millions of species around the world which makes the classification process extremely tedious. This paper introduces a method which utilizes the combination of Local Binary Pattern and Histogram Oriented Gradient as feature extractor for leaf classification which increases the accuracy during classification. Support Vector Machine was used as classifier of the leaf features. Two well-known datasets, Swedish Leaf Dataset and Flavia Dataset, were used to carry out the experimental studies. Our proposed method performed the best when compared to three other methods. � 2019 IEEE. Final 2023-05-29T07:25:15Z 2023-05-29T07:25:15Z 2019 Conference Paper 10.1109/ICSCC.2019.8843650 2-s2.0-85073257532 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073257532&doi=10.1109%2fICSCC.2019.8843650&partnerID=40&md5=21434c487fcf8c6621b65ea9f648da5f https://irepository.uniten.edu.my/handle/123456789/24625 8843650 Institute of Electrical and Electronics Engineers Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Computer vision; Graphic methods; Learning systems; Support vector machines; Classification process; Complex structure; Feature extractor; Histogram of oriented gradients; Leaf classification; Local binary patterns; Medical treatment; Oriented gradients; Classification (of information)
author2 35198314400
author_facet 35198314400
Janahiraman T.V.
Yee L.K.
Der C.S.
Aris H.
format Conference Paper
author Janahiraman T.V.
Yee L.K.
Der C.S.
Aris H.
spellingShingle Janahiraman T.V.
Yee L.K.
Der C.S.
Aris H.
Leaf Classification using Local Binary Pattern and Histogram of Oriented Gradients
author_sort Janahiraman T.V.
title Leaf Classification using Local Binary Pattern and Histogram of Oriented Gradients
title_short Leaf Classification using Local Binary Pattern and Histogram of Oriented Gradients
title_full Leaf Classification using Local Binary Pattern and Histogram of Oriented Gradients
title_fullStr Leaf Classification using Local Binary Pattern and Histogram of Oriented Gradients
title_full_unstemmed Leaf Classification using Local Binary Pattern and Histogram of Oriented Gradients
title_sort leaf classification using local binary pattern and histogram of oriented gradients
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
_version_ 1806426358398582784
score 13.223943

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