Abstract
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.
Highlights
Mobile image retrieval plays an important role in processes such as identification of crop diseases and insect pests, the protection of pedestrians in autonomous vehicles, suspect identification, and medical services [1,2,3,4,5,6]
We study the problem of image retrieval in the multiaccess edge computing (MEC) environment, which is described as follows: As illustrated in Figure 1, the system architecture of MEC consists of three layers of components: user equipment, edge servers, and cloud servers
Compared with using raw images, on the ORL data set, our method reduces the feature matching time by about 69.5% in the case of similar retrieval accuracy. is is because our method extracts a small number of features. erefore, with the same number of images, fewer features result in less feature matching time. is shows that using our method can save a lot of feature matching time
Summary
Mobile image retrieval plays an important role in processes such as identification of crop diseases and insect pests, the protection of pedestrians in autonomous vehicles, suspect identification, and medical services [1,2,3,4,5,6]. It has penetrated into all aspects of people’s lives. Feature extraction and feature matching are two important factors in image retrieval tasks. Feature matching is the most time-consuming, and feature extraction affects the matching time and retrieval results. Hassan et al [13]
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