Journal of Applied Sciences ›› 2018, Vol. 36 ›› Issue (3): 495-503.doi: 10.3969/j.issn.0255-8297.2018.03.009

• Signal and Information Processing • Previous Articles     Next Articles

Restricted Boltzmann Machine Algorithm for Image Classifcation and Its Parallel Implementation Based on (2D)2 PCA

SONG Hai-feng1,2, CHEN Guang-sheng1, JING Wei-peng1, YANG Wei-wei3   

  1. 1. College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China;
    2. College of Computer Science and Technology, Heilongjiang Institute of Technology, Harbin 150050, China;
    3. College of Computer Science and Technology, Harbin Engineering University, Harbin 150000, China
  • Received:2017-09-29 Revised:2017-11-13 Online:2018-05-31 Published:2018-05-31

Abstract:

In this paper, in order to solve the problem of high time complexity when using restricted Boltzmann machine (RBM) to classify the high resolution image, a RBM algorithm for image classifcation based on two-way 2-dimension principal component analysis ((2D)2PCA) is put forward. The algorithm frstly reduces the dimension in X and Z direction on the image by using (2D)2PCA, secondly extracts the principle components as the input data of the visible layer of RBM network, fnally, builds the RBM network with contrastive divergence algorithm and realizes the image classifcation. The proposed algorithm can solve the drawbacks of the long training time of RBM network, which might lead to the convergence failure of the entire network training state as processing the high resolution image. The parallel experimental results show that the algorithm can achieve both high speed and good parallelism as processing high resolution images. The ratio of acceleration reaches 3.13 as employing a cluster of four parallel machines.

Key words: restricted Boltzmann machine (RBM), parallel computing, image classifcation, (2D)2PCA

CLC Number: