A Cloud-Guided Feature Extraction Approach for Image Retrieval in Mobile Edge Computing

Abstract

Mobile Edge Computing (MEC) can facilitate various important image retrieval applications for mobile users by offloading partial computation tasks from resource-limited mobile devices to edge servers. However, existing related works suffer from two major limitations. (i) High network bandwidth cost : they need to extract numerous features from the image and upload these feature data to the cloud server. (ii) Low retrieval accuracy : they separate the feature extraction processes from the image data set in the cloud server, thus unable to provide effective features for accurate image retrieval. In this paper, we propose a cloud-guided feature extraction approach for mobile image retrieval. In the proposed approach, the cloud server first leverages the relationships among labeled images in the data set to learn a projection matrix ${{\mathbf P}}$ P . Then, it uses the matrix ${{\mathbf P}}$ P to extract discriminative features from the image data set and form a low-dimensional feature data set. Following that, the cloud server sends the matrix ${{\mathbf P}}$ P to the edge server and uses it to multiply the image ${{\mathbf x}}$ x . The result ${{\mathbf P}}^T{{\mathbf x}}$ P T x , i.e. , image features, is uploaded to the cloud server to find the label of the image with the most similar multiplying result. The label is regarded as the retrieval result and returned to the mobile user. In the cloud-guided feature extraction approach, the matrix ${{\mathbf P}}$ P can extract a small number of effective image features, which not only reduces network traffic but also improves retrieval accuracy. We have implemented a prototype system to validate the proposed approach and evaluate its performance by conducting extensive experiments using a real MEC environment and data set. The experimental results show that the proposed approach reduces the network traffic by nearly 93 percent and improves the retrieval accuracy by nearly 6.9 percent compared with the state-of-the-art image retrieval approaches in MEC.