Facial image retrieval on semantic features using adaptive mean genetic algorithm

The emergence of larger databases has made image retrieval techniques an essential component, and has led to the development of more efficient image retrieval systems. Retrieval can either be content or text-based. In this paper, the focus is on the content-based image retrieval from the FGNET datab...

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Main Authors:	Shnan, Marwan Ali, Rassem, Taha H., Nor Saradatul Akmar, Zulkifli
Format:	Article
Language:	English
Published:	Universitas Ahmad Dahlan 2019
Subjects:	QA76 Computer software
Online Access:	http://umpir.ump.edu.my/id/eprint/23529/ http://umpir.ump.edu.my/id/eprint/23529/ http://umpir.ump.edu.my/id/eprint/23529/ http://umpir.ump.edu.my/id/eprint/23529/1/facial%20image1.pdf

id	ump-23529
recordtype	eprints
spelling	ump-235292019-01-07T07:12:40Z http://umpir.ump.edu.my/id/eprint/23529/ Facial image retrieval on semantic features using adaptive mean genetic algorithm Shnan, Marwan Ali Rassem, Taha H. Nor Saradatul Akmar, Zulkifli QA76 Computer software The emergence of larger databases has made image retrieval techniques an essential component, and has led to the development of more efficient image retrieval systems. Retrieval can either be content or text-based. In this paper, the focus is on the content-based image retrieval from the FGNET database. Input query images are subjected to several processing techniques in the database before computing the squared Euclidean distance (SED) between them. The images with the shortest Euclidean distance are considered as a match and are retrieved. The processing techniques involve the application of the median modified Weiner filter (MMWF), extraction of the low-level features using histogram-oriented gradients (HOG), discrete wavelet transform (DWT), GIST, and Local tetra pattern (LTrP). Finally, the features are selected using Adaptive Mean Genetic Algorithm (AMGA). In this study, the average PSNR value obtained after applying Wiener filter was 45.29. The performance of the AMGA was evaluated based on its precision, F-measure, and recall, and the obtained average values were respectively 0.75, 0.692, and 0.66. The performance matrix of the AMGA was compared to those of particle swarm optimization algorithm (PSO) and genetic algorithm (GA) and found to perform better; thus, proving its efficiency. Universitas Ahmad Dahlan 2019-07 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/23529/1/facial%20image1.pdf Shnan, Marwan Ali and Rassem, Taha H. and Nor Saradatul Akmar, Zulkifli (2019) Facial image retrieval on semantic features using adaptive mean genetic algorithm. Telkomnika, 17 (2). ISSN 1693-6930 http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/3774 http://dx.doi.org/10.12928/telkomnika.v17i2.3774
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA76 Computer software
spellingShingle	QA76 Computer software Shnan, Marwan Ali Rassem, Taha H. Nor Saradatul Akmar, Zulkifli Facial image retrieval on semantic features using adaptive mean genetic algorithm
description	The emergence of larger databases has made image retrieval techniques an essential component, and has led to the development of more efficient image retrieval systems. Retrieval can either be content or text-based. In this paper, the focus is on the content-based image retrieval from the FGNET database. Input query images are subjected to several processing techniques in the database before computing the squared Euclidean distance (SED) between them. The images with the shortest Euclidean distance are considered as a match and are retrieved. The processing techniques involve the application of the median modified Weiner filter (MMWF), extraction of the low-level features using histogram-oriented gradients (HOG), discrete wavelet transform (DWT), GIST, and Local tetra pattern (LTrP). Finally, the features are selected using Adaptive Mean Genetic Algorithm (AMGA). In this study, the average PSNR value obtained after applying Wiener filter was 45.29. The performance of the AMGA was evaluated based on its precision, F-measure, and recall, and the obtained average values were respectively 0.75, 0.692, and 0.66. The performance matrix of the AMGA was compared to those of particle swarm optimization algorithm (PSO) and genetic algorithm (GA) and found to perform better; thus, proving its efficiency.
format	Article
author	Shnan, Marwan Ali Rassem, Taha H. Nor Saradatul Akmar, Zulkifli
author_facet	Shnan, Marwan Ali Rassem, Taha H. Nor Saradatul Akmar, Zulkifli
author_sort	Shnan, Marwan Ali
title	Facial image retrieval on semantic features using adaptive mean genetic algorithm
title_short	Facial image retrieval on semantic features using adaptive mean genetic algorithm
title_full	Facial image retrieval on semantic features using adaptive mean genetic algorithm
title_fullStr	Facial image retrieval on semantic features using adaptive mean genetic algorithm
title_full_unstemmed	Facial image retrieval on semantic features using adaptive mean genetic algorithm
title_sort	facial image retrieval on semantic features using adaptive mean genetic algorithm
publisher	Universitas Ahmad Dahlan
publishDate	2019
url	http://umpir.ump.edu.my/id/eprint/23529/ http://umpir.ump.edu.my/id/eprint/23529/ http://umpir.ump.edu.my/id/eprint/23529/ http://umpir.ump.edu.my/id/eprint/23529/1/facial%20image1.pdf
first_indexed	2023-09-18T22:35:16Z
last_indexed	2023-09-18T22:35:16Z
_version_	1777416553637085184

Facial image retrieval on semantic features using adaptive mean genetic algorithm

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