EDIR: Efficient Distributed Image Retrieval of Novel Objects in Mobile Networks

Abstract

Crowdsourcing data collection from a network of mobile devices is useful in various applications. Mobile devices store a large amount of visual data that aid in different situations. Trained CNNs can be deployed on mobile devices to be used in searching for objects of interest. Querying for novel objects, for which models have not been trained, presents unique challenges. When novel objects are queried, new models must be trained and distributed to all edge devices, which can be cumbersome. In this paper we propose EDIR, an efficient method and a system that enables answering these queries while taking into account the bandwidth limitations in wireless networks, and the limited energy and computational power on mobile devices. Results show that EDIR reduces the amount of data transfer by 45%compared to other approaches while achieving a good F1 score.