Image Retrieval Using Inception Structure with Hash Layer for Intelligent Monitoring Platform

  • BaoHua Qiang
  • Xina Shi
  • Yufeng Wang
  • Zhi Xu
  • Wu XieEmail author
  • Xianjun Chen
  • Xingchao Zhao
  • Xukang Zhou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11204)


In view of the problem of low efficiency and accuracy in traditional image retrievals, a method using inception structure with hash layers of image retrieval is presented for intelligent monitoring platform. The main idea of our work is to add hash layers into the inception structure of deep neural network, which can be used to transform the global average pooling features into low dimensional binary hash codes. Our method is utilized to not only ensure the sparseness of the neural network, but also avoid the overfitting phenomenon. Experimental results via the MNIST and CIFAR-10 datasets show that the retrieval efficiency and accuracy can be higher using our methods than before.


Image retrieval Inception structure Intelligent platform Deep learning 



This work is supported by the National Marine Technology Program for Public Welfare (No. 201505002), Guangxi Science and Technology Development Project (No. 1598018-6), the National Natural Science Foundation of China under Grant No. 61462020, No. 61762025 and No. 61662014, Guangxi Natural Science Foundation under Grant No. 2017GXNSFAA198226, Guangxi Key Research and Development Program (No. AB17195053), Guangxi Key Laboratory of Trusted Software (kx201510, kx201413), Guangxi Colleges and Universities Key Laboratory of Cloud Computing and Complex System (Nos. 14106, 15204), the Innovation Project of GUET Graduate Education (Nos. 2017YJCX52, 2018YJCX42), Guangxi Cooperative Innovation Center of Cloud Computing and Big Data (Nos. YD16E01, YD16E04, YD1703, YD1712, YD1713, YD1714). Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Image and Graphics (No. GIIP201603).


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • BaoHua Qiang
    • 1
  • Xina Shi
    • 1
  • Yufeng Wang
    • 2
  • Zhi Xu
    • 1
  • Wu Xie
    • 1
    Email author
  • Xianjun Chen
    • 1
  • Xingchao Zhao
    • 1
  • Xukang Zhou
    • 1
  1. 1.Guangxi Key Laboratory of Trusted SoftwareGuilin University of Electronic TechnologyGuilinChina
  2. 2.The 54th Research Institute of China Electronics Technology Group CorporationShijiazhuangChina

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