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IoT 21(28): e4

Research Article

A Review of Image Classification Algorithms in IoT

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  • @ARTICLE{10.4108/eetiot.v7i28.562,
        author={Xiaopeng Zheng and Rayan S Cloutier},
        title={A Review of Image Classification Algorithms in IoT},
        journal={EAI Endorsed Transactions on Internet of Things},
        volume={7},
        number={28},
        publisher={EAI},
        journal_a={IOT},
        year={2022},
        month={4},
        keywords={IOT, Convolutional Neural Network, Image Classification, Deep Learning},
        doi={10.4108/eetiot.v7i28.562}
    }
    
  • Xiaopeng Zheng
    Rayan S Cloutier
    Year: 2022
    A Review of Image Classification Algorithms in IoT
    IOT
    EAI
    DOI: 10.4108/eetiot.v7i28.562
Xiaopeng Zheng1,*, Rayan S Cloutier2
  • 1: Henan Polytechnic University
  • 2: Carleton University
*Contact email: zxp@home.hpu.edu.cn

Abstract

With the advent of big data era and the enhancement of computing power, Deep Learning has swept the world. Based on Convolutional Neural Network (CNN) image classification technique broke the restriction of classical image classification methods, becoming the dominant algorithm of image classification. How to use CNN for image classification has turned into a hot spot. After systematically studying convolutional neural network and in-depth research of the application of CNN in computer vision, this research briefly introduces the mainstream structural models, strengths and shortcomings, time/space complexity, challenges that may be suffered during model training and associated solutions for image classification. This research also compares and analyzes the differences between different methods and their performance on commonly used data sets. Finally, the shortcomings of Deep Learning methods in image classification and possible future research directions are discussed.

Keywords
IOT, Convolutional Neural Network, Image Classification, Deep Learning
Received
2022-03-12
Accepted
2022-04-21
Published
2022-04-21
Publisher
EAI
http://dx.doi.org/10.4108/eetiot.v7i28.562

Copyright © 2022 Xiaopeng Zheng et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.

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