Research Article
Cloud-Edge-Device Collaborative Image Retrieval and Recognition for Mobile Web
@INPROCEEDINGS{10.1007/978-3-031-54528-3_26, author={Yakun Huang and Wenwei Li and Shouyi Wu and Xiuquan Qiao and Meng Guo and Hongshun He and Yang Li}, title={Cloud-Edge-Device Collaborative Image Retrieval and Recognition for Mobile Web}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 19th EAI International Conference, CollaborateCom 2023, Corfu Island, Greece, October 4-6, 2023, Proceedings, Part II}, proceedings_a={COLLABORATECOM PART 2}, year={2024}, month={2}, keywords={Cross-platform Edge computing Image retrieval}, doi={10.1007/978-3-031-54528-3_26} }- Yakun Huang
Wenwei Li
Shouyi Wu
Xiuquan Qiao
Meng Guo
Hongshun He
Yang Li
Year: 2024
Cloud-Edge-Device Collaborative Image Retrieval and Recognition for Mobile Web
COLLABORATECOM PART 2
Springer
DOI: 10.1007/978-3-031-54528-3_26
Abstract
Efficient image retrieval and recognition are pivotal for optimal mobile web vision services. Traditional web-based solutions offer limited accuracy, high overhead, and struggle with vast image volumes. Transferring images for real-time cloud recognition demands stable communication, and large-scale concurrent requests strain computational and network resources. This paper introduces a distributed recognition approach, leveraging cloud-edge-device collaboration through edge computing’s low latency and high bandwidth. We present a lightweight image saliency detection model tailored for mobile web, enhancing initial image feature extraction. Additionally, we introduce an edge-based, deep learning-driven method to amplify image retrieval speed and precision. We incorporate a location and popularity-based caching system to alleviate strains on cloud resources and network bandwidth during extensive image requests. Our real-world tests validate our approach: our saliency detection model outpaces the benchmark by reducing the model size by up to 94%, making it suitable for mobile web deployment. The proposed method improves retrieval accuracy by 40% over cloud-based counterparts and cuts response latency by over 60%.
