Cooperative Cloud-Edge Feature Extraction Architecture for Mobile Image Retrieval

Mobile image retrieval greatly facilitates our lives and works by providing various retrieval services. The existing mobile image retrieval scheme is based on mobile cloud-edge computing architecture. That is, user equipment captures images and uploads the captured image data to the edge server. Aft...

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Autores principales:	Chao He, Gang Ma
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi-Wiley 2021
Materias:	Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/0f2e67f402d142d4a5ee8fdd0cb6edb6
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id	oai:doaj.org-article:0f2e67f402d142d4a5ee8fdd0cb6edb6
record_format	dspace
spelling	oai:doaj.org-article:0f2e67f402d142d4a5ee8fdd0cb6edb62021-11-15T01:20:01ZCooperative Cloud-Edge Feature Extraction Architecture for Mobile Image Retrieval1099-052610.1155/2021/7937922https://doaj.org/article/0f2e67f402d142d4a5ee8fdd0cb6edb62021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/7937922https://doaj.org/toc/1099-0526Mobile image retrieval greatly facilitates our lives and works by providing various retrieval services. The existing mobile image retrieval scheme is based on mobile cloud-edge computing architecture. That is, user equipment captures images and uploads the captured image data to the edge server. After preprocessing these captured image data and extracting features from these image data, the edge server uploads the extracted features to the cloud server. However, the feature extraction on the cloud server is noncooperative with the feature extraction on the edge server which cannot extract features effectively and has a lower image retrieval accuracy. For this, we propose a collaborative cloud-edge feature extraction architecture for mobile image retrieval. The cloud server generates the projection matrix from the image data set with a feature extraction algorithm, and the edge server extracts the feature from the uploaded image with the projection matrix. That is, the cloud server guides the edge server to perform feature extraction. This architecture can effectively extract the image data on the edge server, reduce network load, and save bandwidth. The experimental results indicate that this scheme can upload few features to get high retrieval accuracy and reduce the feature matching time by about 69.5% with similar retrieval accuracy.Chao HeGang MaHindawi-WileyarticleElectronic computers. Computer scienceQA75.5-76.95ENComplexity, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Electronic computers. Computer science QA75.5-76.95
spellingShingle	Electronic computers. Computer science QA75.5-76.95 Chao He Gang Ma Cooperative Cloud-Edge Feature Extraction Architecture for Mobile Image Retrieval
description	Mobile image retrieval greatly facilitates our lives and works by providing various retrieval services. The existing mobile image retrieval scheme is based on mobile cloud-edge computing architecture. That is, user equipment captures images and uploads the captured image data to the edge server. After preprocessing these captured image data and extracting features from these image data, the edge server uploads the extracted features to the cloud server. However, the feature extraction on the cloud server is noncooperative with the feature extraction on the edge server which cannot extract features effectively and has a lower image retrieval accuracy. For this, we propose a collaborative cloud-edge feature extraction architecture for mobile image retrieval. The cloud server generates the projection matrix from the image data set with a feature extraction algorithm, and the edge server extracts the feature from the uploaded image with the projection matrix. That is, the cloud server guides the edge server to perform feature extraction. This architecture can effectively extract the image data on the edge server, reduce network load, and save bandwidth. The experimental results indicate that this scheme can upload few features to get high retrieval accuracy and reduce the feature matching time by about 69.5% with similar retrieval accuracy.
format	article
author	Chao He Gang Ma
author_facet	Chao He Gang Ma
author_sort	Chao He
title	Cooperative Cloud-Edge Feature Extraction Architecture for Mobile Image Retrieval
title_short	Cooperative Cloud-Edge Feature Extraction Architecture for Mobile Image Retrieval
title_full	Cooperative Cloud-Edge Feature Extraction Architecture for Mobile Image Retrieval
title_fullStr	Cooperative Cloud-Edge Feature Extraction Architecture for Mobile Image Retrieval
title_full_unstemmed	Cooperative Cloud-Edge Feature Extraction Architecture for Mobile Image Retrieval
title_sort	cooperative cloud-edge feature extraction architecture for mobile image retrieval
publisher	Hindawi-Wiley
publishDate	2021
url	https://doaj.org/article/0f2e67f402d142d4a5ee8fdd0cb6edb6
work_keys_str_mv	AT chaohe cooperativecloudedgefeatureextractionarchitectureformobileimageretrieval AT gangma cooperativecloudedgefeatureextractionarchitectureformobileimageretrieval
_version_	1718428906177953792

Cooperative Cloud-Edge Feature Extraction Architecture for Mobile Image Retrieval

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