A New Deep Learning-Based Food Recognition System for Dietary Assessment on An Edge Computing Service Infrastructure

By Chang Liu, Yu Cao, Yan Luo, Guanling Chen, Vinod M. Vokkarane, Ma Yunsheng, Songqing Chen, Peng Hou. 2018

297 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Networks and Communications, Public Health, Environmental and Occupational Health

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In Computer Networks and Communications

Citations in the corpus

Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, background): “[82], [128] developed a system to recognize food images and their relevant information, such as types and portion”
All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey (2018, background): “The authors in [36] design a food recognition system based onMCC that distributes the data analytics between themobile devices and the servers in the cloud.”
Communication-Efficient Federated Learning for Wireless Edge Intelligence in IoT (2020, unknown): “[19] created a system for identifying foods, in which image preprocessing and segmentation was performed on edge devices, and classification was done on a central server, reducing the latency at inference time.”
Adaptive Task Allocation for Mobile Edge Learning (2018, background): “e cloud level and over wired distributed computing environments . Extendingthe abovedistributedlearning paradigmto resourceconstrained edge servers and nodes was not explored until very recently. [9]–[11]. The work of [9] explore the possibility of connected mobile android devices in a distributed m”
Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing (2018, background): “Liu et al. [15] proposed the ﬁrst work that introduces deep learning into the edge computing environment.”
Deep Learning With Edge Computing: A Review (2019, background): “[92] built a food recognition system with two preprocessing steps: first, they discard blurry images, and second, they crop the image so that it only contains the objects of interest.”
Edge Computing with Artificial Intelligence: A Machine Learning Perspective (2022, background): “(a) Massive data are preprocessed and then uploaded to the cloud for AI training and reasoning [49].”