Optimized feature selection for tropical wood species recognition using genetic algorithm

An automatic tropical wood species recognition system was developed at the Centre for Artificial Intelligence & Robotics (CAIRO), Universiti Teknologi Malaysia. The system classifies wood species by using texture analysis whereby wood surfaces images are captured and the features are extracted f...

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Main Author:	Khairuddin, Uswah
Format:	Thesis
Language:	English
Published:	2012
Subjects:	QA Mathematics
Online Access:	http://eprints.utm.my/id/eprint/34656/1/UswahKhairuddinMFKE2012.pdf
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id	my-utm-ep.34656
record_format	uketd_dc
spelling	my-utm-ep.346562017-09-25T06:00:22Z Optimized feature selection for tropical wood species recognition using genetic algorithm 2012-10 Khairuddin, Uswah QA Mathematics An automatic tropical wood species recognition system was developed at the Centre for Artificial Intelligence & Robotics (CAIRO), Universiti Teknologi Malaysia. The system classifies wood species by using texture analysis whereby wood surfaces images are captured and the features are extracted from these images which are then used for classifications. The system uses Grey Level Co-occurrence Matrix (GLCM) feature extractor and Back Propagation Neural Network (BPNN) classifier and it can classify 20 wood species. The system performs well with over 90% accuracy. However, when more wood species are added for classification, the accuracy was reduced significantly due to enormous variations among wood. In this thesis, feature selection algorithm by wrapper Genetic Algorithm (GA) was added into the system to overcome features redundancy, making the within class features less discriminatory while increasing the discriminatory features of inter class variations. Basic Grey Level Aura Matrices (BGLAM) and Structural Properties of Pores Distribution (SPPD) feature extractors are used instead of GLCM and the classifiers used are k-Nearest Neighbour and Linear classifiers in Linear Discriminant Analysis (LDA). Results of experiments before and after feature selection for all databases are compared and analysed. The feature selection algorithm shows a considerable improvement in the classification accuracy from 86% to 95%. A new mutation operation in the GA for feature selection is also developed to increase the GA convergence rate while maintaining its level of performance. 2012-10 Thesis http://eprints.utm.my/id/eprint/34656/ http://eprints.utm.my/id/eprint/34656/1/UswahKhairuddinMFKE2012.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:69887?site_name=Restricted Repository masters Universiti Teknologi Malaysia, Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA Mathematics
spellingShingle	QA Mathematics Khairuddin, Uswah Optimized feature selection for tropical wood species recognition using genetic algorithm
description	An automatic tropical wood species recognition system was developed at the Centre for Artificial Intelligence & Robotics (CAIRO), Universiti Teknologi Malaysia. The system classifies wood species by using texture analysis whereby wood surfaces images are captured and the features are extracted from these images which are then used for classifications. The system uses Grey Level Co-occurrence Matrix (GLCM) feature extractor and Back Propagation Neural Network (BPNN) classifier and it can classify 20 wood species. The system performs well with over 90% accuracy. However, when more wood species are added for classification, the accuracy was reduced significantly due to enormous variations among wood. In this thesis, feature selection algorithm by wrapper Genetic Algorithm (GA) was added into the system to overcome features redundancy, making the within class features less discriminatory while increasing the discriminatory features of inter class variations. Basic Grey Level Aura Matrices (BGLAM) and Structural Properties of Pores Distribution (SPPD) feature extractors are used instead of GLCM and the classifiers used are k-Nearest Neighbour and Linear classifiers in Linear Discriminant Analysis (LDA). Results of experiments before and after feature selection for all databases are compared and analysed. The feature selection algorithm shows a considerable improvement in the classification accuracy from 86% to 95%. A new mutation operation in the GA for feature selection is also developed to increase the GA convergence rate while maintaining its level of performance.
format	Thesis
qualification_level	Master's degree
author	Khairuddin, Uswah
author_facet	Khairuddin, Uswah
author_sort	Khairuddin, Uswah
title	Optimized feature selection for tropical wood species recognition using genetic algorithm
title_short	Optimized feature selection for tropical wood species recognition using genetic algorithm
title_full	Optimized feature selection for tropical wood species recognition using genetic algorithm
title_fullStr	Optimized feature selection for tropical wood species recognition using genetic algorithm
title_full_unstemmed	Optimized feature selection for tropical wood species recognition using genetic algorithm
title_sort	optimized feature selection for tropical wood species recognition using genetic algorithm
granting_institution	Universiti Teknologi Malaysia, Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2012
url	http://eprints.utm.my/id/eprint/34656/1/UswahKhairuddinMFKE2012.pdf
_version_	1747816227406872576

Optimized feature selection for tropical wood species recognition using genetic algorithm

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