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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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/7937922 |
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| _version_ | 1832566259375407104 |
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| author | Chao He Gang Ma |
| author_facet | Chao He Gang Ma |
| author_sort | Chao He |
| collection | DOAJ |
| 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 |
| id | doaj-art-0f2e67f402d142d4a5ee8fdd0cb6edb6 |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-0f2e67f402d142d4a5ee8fdd0cb6edb62025-02-03T01:04:40ZengWileyComplexity1099-05262021-01-01202110.1155/2021/7937922Cooperative Cloud-Edge Feature Extraction Architecture for Mobile Image RetrievalChao He0Gang Ma1State Key Laboratory of Network and Switching Technology InstituteSchool of Mathematics and StatisticsMobile 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.http://dx.doi.org/10.1155/2021/7937922 |
| spellingShingle | Chao He Gang Ma Cooperative Cloud-Edge Feature Extraction Architecture for Mobile Image Retrieval Complexity |
| title | 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_short | Cooperative Cloud-Edge Feature Extraction Architecture for Mobile Image Retrieval |
| title_sort | cooperative cloud edge feature extraction architecture for mobile image retrieval |
| url | http://dx.doi.org/10.1155/2021/7937922 |
| work_keys_str_mv | AT chaohe cooperativecloudedgefeatureextractionarchitectureformobileimageretrieval AT gangma cooperativecloudedgefeatureextractionarchitectureformobileimageretrieval |