Multimodal computer image recognition based on depth neural network

  • Shan Wang
  • YueXing Wang
  • HuiLing Shan
  • ChunXiang Shi


In this paper, multi-modal computer image is identified based on deep neural network. First, we propose two rules for reduction. The first reduction rule is used to reduce the number of similarity calculations between adjacent vertices. The second reduction rule reduces calculation time by inaccurately calculating the similarity between adjacent vertices. Secondly, a structured graph based on GraphX in Spark is proposed Class Algorithm GXDSGC. The algorithm does not require a large amount of disk I/O overhead during operation. Finally, experiments on a large number of real data sets and synthetic data sets, confirm the effectiveness of the proposed GXDSGC algorithm. The GXDSGC algorithm is more than 30 times faster than the algorithm based on the MapReduce framework in Hadoop, which can significantly improve the efficiency of computer image recognition in big data analysis.


Cloud computing GXDSGC algorithm Distributed architecture 


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Shan Wang
    • 1
  • YueXing Wang
    • 1
  • HuiLing Shan
    • 1
  • ChunXiang Shi
    • 2
  1. 1.School of Information EngineeringEast China Jiao Tong UniversityNanchangChina
  2. 2.National Meteorological Information CenterBeijingChina

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