Trademark image classification approaches using neural network and rough set theory

The critical step in automatic trademark matching is to extract trademark features from the database automatically and reliably. However, the performance of existing algorithms rely heavily on the size of the database. It is essential to incorporate an eficient classification technique to partition...

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Main Author:	Saad, Puteh
Format:	Thesis
Language:	English
Published:	2003
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/6830/1/PutehSaadPFC2003.pdf
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id	my-utm-ep.6830
record_format	uketd_dc
spelling	my-utm-ep.68302018-09-27T04:03:51Z Trademark image classification approaches using neural network and rough set theory 2003-08 Saad, Puteh QA75 Electronic computers. Computer science The critical step in automatic trademark matching is to extract trademark features from the database automatically and reliably. However, the performance of existing algorithms rely heavily on the size of the database. It is essential to incorporate an eficient classification technique to partition the database in order to ensure the performance of an automatic trademark matching system is robust with respect to the increase in the database size. Two new approaches are proposed to classify trademark images. The approaches contain five major stages, namely: image acquisition, image preprocessing, feature extraction, data transformation and classification. Feature normalization and data discretization techniques are utilized to perform the data transformation phase. An Adaptive Multi Layer Perceptron (MLP) embedded with an enhanced Backpropagation (BP) algorithm and Rough Set Theory are applied to classify the images. Experimental results reveal that the Adaptive MLP embedded with the enhanced BP algorithm exhibits a faster convergence rate than the classical BP algorithm. In conclusion, the Adaptive MLP outperforms Rough Set Theory in terms of speed, accuracy and sample size. 2003-08 Thesis http://eprints.utm.my/id/eprint/6830/ http://eprints.utm.my/id/eprint/6830/1/PutehSaadPFC2003.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:62458 phd doctoral Universiti Teknologi Malaysia, Faculty of Computer Science and Information System Faculty of Computer Science and Information System
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Saad, Puteh Trademark image classification approaches using neural network and rough set theory
description	The critical step in automatic trademark matching is to extract trademark features from the database automatically and reliably. However, the performance of existing algorithms rely heavily on the size of the database. It is essential to incorporate an eficient classification technique to partition the database in order to ensure the performance of an automatic trademark matching system is robust with respect to the increase in the database size. Two new approaches are proposed to classify trademark images. The approaches contain five major stages, namely: image acquisition, image preprocessing, feature extraction, data transformation and classification. Feature normalization and data discretization techniques are utilized to perform the data transformation phase. An Adaptive Multi Layer Perceptron (MLP) embedded with an enhanced Backpropagation (BP) algorithm and Rough Set Theory are applied to classify the images. Experimental results reveal that the Adaptive MLP embedded with the enhanced BP algorithm exhibits a faster convergence rate than the classical BP algorithm. In conclusion, the Adaptive MLP outperforms Rough Set Theory in terms of speed, accuracy and sample size.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Saad, Puteh
author_facet	Saad, Puteh
author_sort	Saad, Puteh
title	Trademark image classification approaches using neural network and rough set theory
title_short	Trademark image classification approaches using neural network and rough set theory
title_full	Trademark image classification approaches using neural network and rough set theory
title_fullStr	Trademark image classification approaches using neural network and rough set theory
title_full_unstemmed	Trademark image classification approaches using neural network and rough set theory
title_sort	trademark image classification approaches using neural network and rough set theory
granting_institution	Universiti Teknologi Malaysia, Faculty of Computer Science and Information System
granting_department	Faculty of Computer Science and Information System
publishDate	2003
url	http://eprints.utm.my/id/eprint/6830/1/PutehSaadPFC2003.pdf
_version_	1747814694983303168

Trademark image classification approaches using neural network and rough set theory

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