SMART CITY SECURITY: FACE-BASED IMAGE RETRIEVAL MODEL USING GRAY LEVEL CO-OCCURRENCE MATRIX

Nowadays, a lot of images and documents are saved on data sets and cloud servers such as certificates, personal images, and passports. These images and documents are utilized in several applications to serve residents living in smart cities. Image similarity is considered as one of the applications...

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Autores principales:	Abdullah Mohammed Rashid, Ali A. Yassin, Ahmed A. Abdel Wahed, Abdulla J. Yassin
Formato:	article
Lenguaje:	EN
Publicado:	UUM Press 2020
Materias:	image retrieval image similarity extracted features smart city security Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/773375096bb3462ebef74208b99e16c1
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spelling	oai:doaj.org-article:773375096bb3462ebef74208b99e16c12021-11-15T04:11:52ZSMART CITY SECURITY: FACE-BASED IMAGE RETRIEVAL MODEL USING GRAY LEVEL CO-OCCURRENCE MATRIX10.32890/jict2020.19.3.61675-414X2180-3862https://doaj.org/article/773375096bb3462ebef74208b99e16c12020-06-01T00:00:00Zhttp://e-journal.uum.edu.my/index.php/jict/article/view/jict2020.19.3.6https://doaj.org/toc/1675-414Xhttps://doaj.org/toc/2180-3862Nowadays, a lot of images and documents are saved on data sets and cloud servers such as certificates, personal images, and passports. These images and documents are utilized in several applications to serve residents living in smart cities. Image similarity is considered as one of the applications of smart cities. The major challenges faced in the field of image management are searching and retrieving images. This is because searching based on image content requires a long time. In this paper, the researchers present a secure scheme to retrieve images in smart cities to identify wanted criminals by using the Gray Level Co-occurrence Matrix. The proposed scheme extracts only five features of the query image which are contrast, homogeneity, entropy, energy, and dissimilarity. This work consists of six phases which are registration, authentication, face detection, features extraction, image similarity, and image retrieval. The current study runs on a database of 810 images which was borrowed from face94 to measure the performance of image retrieval. The results of the experiment showed that the average precision is 97.6 and average recall is 6.3., Results of the current study have been relatively inspiring compared with the results of two previous studies. Abdullah Mohammed RashidAli A. YassinAhmed A. Abdel WahedAbdulla J. YassinUUM Pressarticleimage retrievalimage similarityextracted featuressmart citysecurityInformation technologyT58.5-58.64ENJournal of ICT, Vol 19, Iss 3, Pp 437-458 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	image retrieval image similarity extracted features smart city security Information technology T58.5-58.64
spellingShingle	image retrieval image similarity extracted features smart city security Information technology T58.5-58.64 Abdullah Mohammed Rashid Ali A. Yassin Ahmed A. Abdel Wahed Abdulla J. Yassin SMART CITY SECURITY: FACE-BASED IMAGE RETRIEVAL MODEL USING GRAY LEVEL CO-OCCURRENCE MATRIX
description	Nowadays, a lot of images and documents are saved on data sets and cloud servers such as certificates, personal images, and passports. These images and documents are utilized in several applications to serve residents living in smart cities. Image similarity is considered as one of the applications of smart cities. The major challenges faced in the field of image management are searching and retrieving images. This is because searching based on image content requires a long time. In this paper, the researchers present a secure scheme to retrieve images in smart cities to identify wanted criminals by using the Gray Level Co-occurrence Matrix. The proposed scheme extracts only five features of the query image which are contrast, homogeneity, entropy, energy, and dissimilarity. This work consists of six phases which are registration, authentication, face detection, features extraction, image similarity, and image retrieval. The current study runs on a database of 810 images which was borrowed from face94 to measure the performance of image retrieval. The results of the experiment showed that the average precision is 97.6 and average recall is 6.3., Results of the current study have been relatively inspiring compared with the results of two previous studies.
format	article
author	Abdullah Mohammed Rashid Ali A. Yassin Ahmed A. Abdel Wahed Abdulla J. Yassin
author_facet	Abdullah Mohammed Rashid Ali A. Yassin Ahmed A. Abdel Wahed Abdulla J. Yassin
author_sort	Abdullah Mohammed Rashid
title	SMART CITY SECURITY: FACE-BASED IMAGE RETRIEVAL MODEL USING GRAY LEVEL CO-OCCURRENCE MATRIX
title_short	SMART CITY SECURITY: FACE-BASED IMAGE RETRIEVAL MODEL USING GRAY LEVEL CO-OCCURRENCE MATRIX
title_full	SMART CITY SECURITY: FACE-BASED IMAGE RETRIEVAL MODEL USING GRAY LEVEL CO-OCCURRENCE MATRIX
title_fullStr	SMART CITY SECURITY: FACE-BASED IMAGE RETRIEVAL MODEL USING GRAY LEVEL CO-OCCURRENCE MATRIX
title_full_unstemmed	SMART CITY SECURITY: FACE-BASED IMAGE RETRIEVAL MODEL USING GRAY LEVEL CO-OCCURRENCE MATRIX
title_sort	smart city security: face-based image retrieval model using gray level co-occurrence matrix
publisher	UUM Press
publishDate	2020
url	https://doaj.org/article/773375096bb3462ebef74208b99e16c1
work_keys_str_mv	AT abdullahmohammedrashid smartcitysecurityfacebasedimageretrievalmodelusinggraylevelcooccurrencematrix AT aliayassin smartcitysecurityfacebasedimageretrievalmodelusinggraylevelcooccurrencematrix AT ahmedaabdelwahed smartcitysecurityfacebasedimageretrievalmodelusinggraylevelcooccurrencematrix AT abdullajyassin smartcitysecurityfacebasedimageretrievalmodelusinggraylevelcooccurrencematrix
_version_	1718428872951726080

SMART CITY SECURITY: FACE-BASED IMAGE RETRIEVAL MODEL USING GRAY LEVEL CO-OCCURRENCE MATRIX

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