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Tree species recognition system based on macroscopic image analysis

An automated wood texture recognition system of 48 tropical wood species is presented. For each wood species, 100 macroscopic texture images are captured from different timber logs where 70 images are used for training while 30 images are used for testing. In this work, a fuzzy pre-classifier is use...

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Main Authors:	Ibrahim, I., Khairuddin, A. S. M., Abu Talip, M. S., Arof, H., Yusof, R.
Format:	Article
Published:	Springer Verlag 2017
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/75533/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988430445&doi=10.1007%2fs00226-016-0859-4&partnerID=40&md5=5bf233e6bcdb57ec1db8cf1e34134a7e
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spelling	my.utm.755332018-04-12T05:43:16Z http://eprints.utm.my/id/eprint/75533/ Tree species recognition system based on macroscopic image analysis Ibrahim, I. Khairuddin, A. S. M. Abu Talip, M. S. Arof, H. Yusof, R. TK Electrical engineering. Electronics Nuclear engineering An automated wood texture recognition system of 48 tropical wood species is presented. For each wood species, 100 macroscopic texture images are captured from different timber logs where 70 images are used for training while 30 images are used for testing. In this work, a fuzzy pre-classifier is used to complement a set of support vector machines (SVM) to manage the large wood database and classify the wood species efficiently. Given a test image, a set of texture pore features is extracted from the image and used as inputs to a fuzzy pre-classifier which assigns it to one of the four broad categories. Then, another set of texture features is extracted from the image and used with the SVM dedicated to the selected category to further classify the test image to a particular wood species. The advantage of dividing the database into four smaller databases is that when a new wood species is added into the system, only the SVM classifier of one of the four databases needs to be retrained instead of those of the entire database. This shortens the training time and emulates the experts’ reasoning when expanding the wood database. The results show that the proposed model is more robust as the size of wood database is increased. Springer Verlag 2017 Article PeerReviewed Ibrahim, I. and Khairuddin, A. S. M. and Abu Talip, M. S. and Arof, H. and Yusof, R. (2017) Tree species recognition system based on macroscopic image analysis. Wood Science and Technology, 51 (2). pp. 431-444. ISSN 0043-7719 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988430445&doi=10.1007%2fs00226-016-0859-4&partnerID=40&md5=5bf233e6bcdb57ec1db8cf1e34134a7e
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Ibrahim, I. Khairuddin, A. S. M. Abu Talip, M. S. Arof, H. Yusof, R. Tree species recognition system based on macroscopic image analysis
description	An automated wood texture recognition system of 48 tropical wood species is presented. For each wood species, 100 macroscopic texture images are captured from different timber logs where 70 images are used for training while 30 images are used for testing. In this work, a fuzzy pre-classifier is used to complement a set of support vector machines (SVM) to manage the large wood database and classify the wood species efficiently. Given a test image, a set of texture pore features is extracted from the image and used as inputs to a fuzzy pre-classifier which assigns it to one of the four broad categories. Then, another set of texture features is extracted from the image and used with the SVM dedicated to the selected category to further classify the test image to a particular wood species. The advantage of dividing the database into four smaller databases is that when a new wood species is added into the system, only the SVM classifier of one of the four databases needs to be retrained instead of those of the entire database. This shortens the training time and emulates the experts’ reasoning when expanding the wood database. The results show that the proposed model is more robust as the size of wood database is increased.
format	Article
author	Ibrahim, I. Khairuddin, A. S. M. Abu Talip, M. S. Arof, H. Yusof, R.
author_facet	Ibrahim, I. Khairuddin, A. S. M. Abu Talip, M. S. Arof, H. Yusof, R.
author_sort	Ibrahim, I.
title	Tree species recognition system based on macroscopic image analysis
title_short	Tree species recognition system based on macroscopic image analysis
title_full	Tree species recognition system based on macroscopic image analysis
title_fullStr	Tree species recognition system based on macroscopic image analysis
title_full_unstemmed	Tree species recognition system based on macroscopic image analysis
title_sort	tree species recognition system based on macroscopic image analysis
publisher	Springer Verlag
publishDate	2017
url	http://eprints.utm.my/id/eprint/75533/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988430445&doi=10.1007%2fs00226-016-0859-4&partnerID=40&md5=5bf233e6bcdb57ec1db8cf1e34134a7e
_version_	1643657089341456384
score	13.211869

Tree species recognition system based on macroscopic image analysis

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