Cloud-Edge-Device Collaborative Image Retrieval and Recognition for Mobile Web

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%.