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Research on Parallel Mining Method of Massive Image Data Based on AI

  • Shuang-cheng JiaEmail author
  • Feng-ping Yang
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 302)

Abstract

Parallel mining of image data is based on the extraction of internal rules and detail features of image. Combined with image edge detection to realize parallel mining of image data, a parallel mining algorithm of image data based on AI is proposed. Firstly, the multidimensional parallel eigenvalues of image data are extracted by the gray feature extraction algorithm of massive images, and then the template matching and information fusion of massive image data are carried out by using Map/Reduce model. According to the matching results, the parallel mining results of image data are obtained. Finally, the simulation experiment of image data parallel mining is realized by using Matlab software. The results show that compared with other image data parallel mining algorithms, this algorithm reduces the parallel mining time of image data and improves the speed of image data parallel mining, especially for large-scale image data parallel mining.

Keywords

AI Massive image data Parallel mining Template matching 

References

  1. 1.
    Zhou, S.B., Xu, W.X.: A novel clustering algorithm based on relative density and decision graph. Control Decis. 33(11), 1921–1930 (2018)zbMATHGoogle Scholar
  2. 2.
    Zhu, Y., Zhu, X., Wang, J.: Time series motif discovery algorithm based on subsequence full join and maximum clique. J. Comput. Appl. 39(2), 414–420 (2019)Google Scholar
  3. 3.
    Wang, Z., Huang, M., et al.: Integrated algorithm based on density peaks and density-based clustering. J. Comput. Appl. 39(2), 398–402 (2019)Google Scholar
  4. 4.
    Liu, Y., Yang, H., Cai, S., Zhang, C.: Single image super-resolution reconstruction method based on improved convolutional neural network. J. Comput. Appl. 39(5), 1440–1447 (2019)Google Scholar
  5. 5.
    Xu, R., Zhang, J.G., Huang, K.Q.: Image super-resolution using two-channel convolutional neural networks. J. Image Graph. 21(5), 556–564 (2016)Google Scholar
  6. 6.
    Megha, G., Yashpal, L., Vivek, L.: Analytical relation & comparison of PSNR and SSIM on Babbon image and human eye perception using Matlab. Int. J. Adv. Res. Eng. Appl. Sci. 4(5), 108–119 (2015)Google Scholar
  7. 7.
    Li, Y.F., Fu, R.D., Jin, W., et al.: Image super-resolution using multi-channel convolution. J. Image Graph. 22(12), 1690–1700 (2017)Google Scholar
  8. 8.
    Dong, X.W., Yao, S.M., Wang, Y.W., et al.: Virtual sample image set based multi manifold discriminant learning algorithm. Appl. Res. Comput. 35(6), 1871–1878 (2018)Google Scholar
  9. 9.
    Gui, J., Sun, Z.N., Jia, W., et al.: Discriminant sparse neighborhood preserving embedding for face recognition. Pattern Recognit. 45(8), 2884–2893 (2012)CrossRefGoogle Scholar
  10. 10.
    Dong, B., Chen, D., Jing, W.: Salient region detection method based on symmetric region filtering. Comput. Eng. 45(5), 216–221 (2019)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  1. 1.Alibaba Network Technology Co., Ltd.BeijingChina

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