An Ar Natural Marker Similarities Measurement Algorithm For E-Biodiversity

In the last few decades, different techniques have been studied and proposed for flower species classification. Nonetheless, the outcomes of these research are particular in term of the assessed stages of classification conduit, the adopted data for assessments, and in the comparative baseline me...

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Main Authors: Tan, Mei Synn, Wang, Yin Chai
Format: Proceeding
Language:English
Published: 2018
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Online Access:http://ir.unimas.my/id/eprint/26739/1/AN%20AR%20NATURAL.pdf
http://ir.unimas.my/id/eprint/26739/
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spelling my.unimas.ir.267392021-12-04T04:49:42Z http://ir.unimas.my/id/eprint/26739/ An Ar Natural Marker Similarities Measurement Algorithm For E-Biodiversity Tan, Mei Synn Wang, Yin Chai QA75 Electronic computers. Computer science In the last few decades, different techniques have been studied and proposed for flower species classification. Nonetheless, the outcomes of these research are particular in term of the assessed stages of classification conduit, the adopted data for assessments, and in the comparative baseline methods. The objective of this research is to comparatively evaluate the effectiveness of different algorithms, method combination procedure, and their parameters towards classification accuracy. Algorithms of investigation starting with span from extraction, matching and classification to determine the interest point of flower species, like colour and shape features information. This research has been found out that the feature extraction process in Augmented Reality (AR) system can be combined into Content-Based Image Retrieval (CBIR) system yield higher classification results in efficiency and accuracy. The accurate identification of image features can reduce the computational complexity, time consuming and enhance the accuracy of the identification and classification for flower species. The proposed method can successfully reduce the number of interest point by 89.98 percent. In addition, the computational complexity can be reduced from O(n log n) to O(n), and the percentage of average accuracy for classifying flower species had reached 98.8 percent. 2018 Proceeding PeerReviewed text en http://ir.unimas.my/id/eprint/26739/1/AN%20AR%20NATURAL.pdf Tan, Mei Synn and Wang, Yin Chai (2018) An Ar Natural Marker Similarities Measurement Algorithm For E-Biodiversity. In: INNOVATION TECHNOLOGY EXPO (INTEX) 2018 CON FERENCE, 17-18 July 2018, Kuching , Sarawak, Malaysia.
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Tan, Mei Synn
Wang, Yin Chai
An Ar Natural Marker Similarities Measurement Algorithm For E-Biodiversity
description In the last few decades, different techniques have been studied and proposed for flower species classification. Nonetheless, the outcomes of these research are particular in term of the assessed stages of classification conduit, the adopted data for assessments, and in the comparative baseline methods. The objective of this research is to comparatively evaluate the effectiveness of different algorithms, method combination procedure, and their parameters towards classification accuracy. Algorithms of investigation starting with span from extraction, matching and classification to determine the interest point of flower species, like colour and shape features information. This research has been found out that the feature extraction process in Augmented Reality (AR) system can be combined into Content-Based Image Retrieval (CBIR) system yield higher classification results in efficiency and accuracy. The accurate identification of image features can reduce the computational complexity, time consuming and enhance the accuracy of the identification and classification for flower species. The proposed method can successfully reduce the number of interest point by 89.98 percent. In addition, the computational complexity can be reduced from O(n log n) to O(n), and the percentage of average accuracy for classifying flower species had reached 98.8 percent.
format Proceeding
author Tan, Mei Synn
Wang, Yin Chai
author_facet Tan, Mei Synn
Wang, Yin Chai
author_sort Tan, Mei Synn
title An Ar Natural Marker Similarities Measurement Algorithm For E-Biodiversity
title_short An Ar Natural Marker Similarities Measurement Algorithm For E-Biodiversity
title_full An Ar Natural Marker Similarities Measurement Algorithm For E-Biodiversity
title_fullStr An Ar Natural Marker Similarities Measurement Algorithm For E-Biodiversity
title_full_unstemmed An Ar Natural Marker Similarities Measurement Algorithm For E-Biodiversity
title_sort ar natural marker similarities measurement algorithm for e-biodiversity
publishDate 2018
url http://ir.unimas.my/id/eprint/26739/1/AN%20AR%20NATURAL.pdf
http://ir.unimas.my/id/eprint/26739/
_version_ 1718930097918967808
score 13.211869