Research article Special Issues

A location-aware feature extraction algorithm for image recognition in mobile edge computing

  • Received: 02 April 2019 Accepted: 19 June 2019 Published: 22 July 2019
  • With the explosive growth of mobile devices, it is feasible to deploy image recognition applications on mobile devices to provide image recognition services. However, traditional mobile cloud computing architecture cannot meet the demands of real time response and high accuracy since users require to upload raw images to the remote central cloud servers. The emerging architecture, Mobile Edge Computing (MEC) deploys small scale servers at the edge of the network, which can provide computing and storage resources for image recognition applications. To this end, in this paper, we aim to use the MEC architecture to provide image recognition service. Moreover, in order to guarantee the real time response and high accuracy, we also provide a feature extraction algorithm to extract discriminative features from the raw image to improve the accuracy of the image recognition applications. In doing so, the response time can be further reduced and the accuracy can be improved. The experimental results show that the combination between MEC architecture and the proposed feature extraction algorithm not only can greatly reduce the response time, but also improve the accuracy of the image recognition applications.

    Citation: Tianjun Lu, Xian Zhong, Luo Zhong, RuiqiLuo. A location-aware feature extraction algorithm for image recognition in mobile edge computing[J]. Mathematical Biosciences and Engineering, 2019, 16(6): 6672-6682. doi: 10.3934/mbe.2019332

    Related Papers:

  • With the explosive growth of mobile devices, it is feasible to deploy image recognition applications on mobile devices to provide image recognition services. However, traditional mobile cloud computing architecture cannot meet the demands of real time response and high accuracy since users require to upload raw images to the remote central cloud servers. The emerging architecture, Mobile Edge Computing (MEC) deploys small scale servers at the edge of the network, which can provide computing and storage resources for image recognition applications. To this end, in this paper, we aim to use the MEC architecture to provide image recognition service. Moreover, in order to guarantee the real time response and high accuracy, we also provide a feature extraction algorithm to extract discriminative features from the raw image to improve the accuracy of the image recognition applications. In doing so, the response time can be further reduced and the accuracy can be improved. The experimental results show that the combination between MEC architecture and the proposed feature extraction algorithm not only can greatly reduce the response time, but also improve the accuracy of the image recognition applications.


    加载中


    [1] S. Bera, S. Misra and J. J. P. C. Rodrigues, Cloud computing applications for smart grid: A survey, IEEE T. Parall. Distr., 26 (2015), 1477–1494.
    [2] C. Esposito, A. Castiglione, B. Martini, et al., Cloud manufacturing: security, privacy, and forensic concerns, IEEE Cloud Comput., 3 (2016), 16–22.
    [3] Y. C. Hu, M. Patel, D. Sabella, et al., Mobile edge computing: A key technology towards 5G, ETSI white paper, 11 (2015), 1–16.
    [4] M. T. Liu, F. R. Yu, Y. L. Teng, et al., Distributed resource allocation in blockchain-based video streaming systems with mobile edge computing, IEEE T. Wirel. Commun., 5 (2019), 695–708.
    [5] S. Wang, Y. Zhao, J. Xu, et al., Edge Server Placement in Mobile Edge Computing, Journal of Parallel and Distributed Computing, 2018. Available from: https://www.sciencedirect.com/science/article/pii/S0743731518304398.
    [6] S. Wang, Y. Zhao, L. Huang, et al., QoS Prediction for Service Recommendations in Mobile Edge Computing, Journal of Parallel and Distributed Computing, 2017. Available from: http://www.sciencedirect.com/science/article/pii/S074373151730268X.
    [7] H. T. Zhao, S. Y. Sun, Z. L. Jing, et al., Local structure based supervised feature extraction, Pattern Recognition, 39 (2005), 1546–1550.
    [8] W. Zhang, X. Y. Xue, H. Lu, et al., Discriminant neighborhood embedding for classification, Pattern Recognition, 39 (2006), 2240–2243.
    [9] S. C. Yan, D. Xu, B. Y. Zhang, et al., Graph embedding: a general framework for dimensionality reduction, In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2005), 830–837.
    [10] S. C. Yan, D. Xu, B. Y. Zhang, et al., Graph embedding and extensions: a general framework for dimensionality reduction, IEEE T. Pattern Anal., 29 (2007), 40–51.
    [11] C. T. Ding and S. G. Wang, Appropriate points choosing for subspace learning over image classification, J. Supercomput., 75 (2018), 688–703.
    [12] C. T. Ding and L. Zhang, Double adjacency graphs-based discriminant neighborhood embedding, Pattern Recognition, 48 (2015), 1734–1742.
    [13] Y. C. Hu, M. Patel, D. Sabella, et al., Mobile edge computing: A key technology towards 5G, ETSI white paper, 11 (2015), 1–16.
    [14] N. Abbas, Y. Zhang, A. Taherkordi, et al., Mobile edge computing: a survey, IEEE Internet Things, 5 (2018), 450–465.
    [15] E. Ahmed, A. Naveed, A. Gani, et al., Process state synchronization-based application execution management for mobile edge/cloud computing, Future Gener. Comp. Sy., 91 (2019), 579–589.
    [16] Y. M. Zhang, X. L. Lan, Y. Li, et al., Efficient Computation Resource Management in Mobile Edge-Cloud Computing, IEEE Internet Things, 6 (2019), 3455–3466.
    [17] J. Zhang, L. Zhou, Q. Tang, et al., Stochastic Computation Offloading and Trajectory Scheduling for UAV-Assisted Mobile Edge Computing, IEEE Internet Things, 6 (2019), 3688–3699.
  • Reader Comments
  • © 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(4179) PDF downloads(479) Cited by(3)

Article outline

Figures and Tables

Figures(7)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog