Feature selection for content-based image retrieval using statistical discriminant analysis

As we known, the very large repository of digital media arise the challenge of various digital search applications. In order to make use of this huge amount of data, effective tools are required for retrieve multimedia information. An image retrieval system is one of the tools that can be used for s...

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Main Author:	Tee, Cheng Siew
Format:	Thesis
Language:	English
Published:	2008
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/9466/1/TeeChengSiewFSKSM2008.pdf
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id	my-utm-ep.9466
record_format	uketd_dc
spelling	my-utm-ep.94662018-07-19T01:38:58Z Feature selection for content-based image retrieval using statistical discriminant analysis 2008-10 Tee, Cheng Siew QA75 Electronic computers. Computer science As we known, the very large repository of digital media arise the challenge of various digital search applications. In order to make use of this huge amount of data, effective tools are required for retrieve multimedia information. An image retrieval system is one of the tools that can be used for searching and retrieving images from a large database of digital images. However, there are several challenges and problems need to be considered when applied image retrieval system such as the gap between high-level semantic concept and low-level visual features. This refers to problem of feature selection, which is critical to really solve the gap problem in CBIR. Recently, the most feasible feature selection method is discriminant analysis. Therefore, in this project, we proposed title feature selection in content-based image retrieval using statistical discriminant analysis. In the project, we intended to enhance performance by improve the feature selection process. Besides, we used fuzzy theory in content-based image retrieval to solve the problem of perspective subjectivity of human in image retrieval. The system would be more depends to the human-like and how to response with relevant images that match the concept of current query is always the research question in this project. 2008-10 Thesis http://eprints.utm.my/id/eprint/9466/ http://eprints.utm.my/id/eprint/9466/1/TeeChengSiewFSKSM2008.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:687?site_name=Restricted Repository masters 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 Tee, Cheng Siew Feature selection for content-based image retrieval using statistical discriminant analysis
description	As we known, the very large repository of digital media arise the challenge of various digital search applications. In order to make use of this huge amount of data, effective tools are required for retrieve multimedia information. An image retrieval system is one of the tools that can be used for searching and retrieving images from a large database of digital images. However, there are several challenges and problems need to be considered when applied image retrieval system such as the gap between high-level semantic concept and low-level visual features. This refers to problem of feature selection, which is critical to really solve the gap problem in CBIR. Recently, the most feasible feature selection method is discriminant analysis. Therefore, in this project, we proposed title feature selection in content-based image retrieval using statistical discriminant analysis. In the project, we intended to enhance performance by improve the feature selection process. Besides, we used fuzzy theory in content-based image retrieval to solve the problem of perspective subjectivity of human in image retrieval. The system would be more depends to the human-like and how to response with relevant images that match the concept of current query is always the research question in this project.
format	Thesis
qualification_level	Master's degree
author	Tee, Cheng Siew
author_facet	Tee, Cheng Siew
author_sort	Tee, Cheng Siew
title	Feature selection for content-based image retrieval using statistical discriminant analysis
title_short	Feature selection for content-based image retrieval using statistical discriminant analysis
title_full	Feature selection for content-based image retrieval using statistical discriminant analysis
title_fullStr	Feature selection for content-based image retrieval using statistical discriminant analysis
title_full_unstemmed	Feature selection for content-based image retrieval using statistical discriminant analysis
title_sort	feature selection for content-based image retrieval using statistical discriminant analysis
granting_institution	Universiti Teknologi Malaysia, Faculty of Computer Science and Information System
granting_department	Faculty of Computer Science and Information System
publishDate	2008
url	http://eprints.utm.my/id/eprint/9466/1/TeeChengSiewFSKSM2008.pdf
_version_	1747814736722919424

Feature selection for content-based image retrieval using statistical discriminant analysis

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