中文

Journal of Applied Sciences >

2018 , Vol. 36 >Issue 3: 495 - 503

DOI: https://doi.org/10.3969/j.issn.0255-8297.2018.03.009

Signal and Information Processing

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

Expand
  • 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 date: 2017-09-29

  Revised date: 2017-11-13

  Online published: 2018-05-31

Fold

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

Cite this article

SONG Hai-feng, CHEN Guang-sheng, JING Wei-peng, YANG Wei-wei . Restricted Boltzmann Machine Algorithm for Image Classifcation and Its Parallel Implementation Based on (2D)2 PCA[J]. Journal of Applied Sciences, 2018 , 36(3) : 495 -503 . DOI: 10.3969/j.issn.0255-8297.2018.03.009

References

[1] Flusser J, Suk T. Pattern recognition by afne moment invariants[J]. Pattern Recognition, 1993, 26(1):167-174.
[2] Ren J, Li X, Haupt J. Robust PCA via tensor outlier pursuit[C]//Conference on Signals, Systems & Computers, 2017:1744-1749.
[3] Lu M, Huang J Z, Qian X. Sparse exponential family principal component analysis[J]. Pattern Recognition, 2016, 60:681-691.
[4] Cheng Z D, Zhang Y J, Fan X. Criteria for 2DPCA superior to PCA in image feature extraction[J]. Chinese Journal of Engineering Mathematics, 2009, 26(6):951-961.
[5] Shi Z G. A human face recognition method of 2DPCA based on modular residual image[J]. Journal of Inner Mongolia Normal University, 2015, 44(3):380-384.
[6] Wang H X. 2DPCA with L1-norm for simultaneously robust and sparse modelling[J]. Neural Networks, 2013, 46(10):190-198.
[7] Li W. The image feature extraction algorithm based on the DWT and the improved 2DPCA[J]. Applied Mechanics and Materials, 2014, 556-562:5042-5045.
[8] Yang J, Zhang D, Frangi A F, Yang J Y. Two-dimensional PCA:a new approach to appearance-based face representation and recognition[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2004, 26(1):131-137.
[9] Yang J, Yang J Y. From image vector to matrix:a straightforward image projection techniqueIMPCA vs. PCA[J]. Pattern Recognition, 2002, 35(9):1997-1999
[10] Liu S, Feng L, Qiao H. Scatter balance:an angle-based supervised dimensionality reduction[J]. IEEE Transactions on Neural Networks & Learning Systems, 2015, 26(2):277-289.
[11] Mahanta M S, Plataniotis K N. Ranking 2DLDA features based on fsher discriminance[J]. IEEE International Conference on Acoustics, 2014:8307-8311.
[12] Mashhoori A, Zolghadri M J. Block-wise two-directional 2DPCA with ensemble learning for face recognition[J]. Neurocomputing, 2013, 108(5):111-117.
[13] Längkvist M, Karlsson L, Loutfi A. A review of unsupervised feature learning and deep learning for time-series modeling[J]. Pattern Recognition Letters, 2014, 42(1):11-24.
[14] Krizhevsky A, Sutskever I, Hinton G E. Imagenet classifcation with deep convolutional neural networks[J]. International Conference on Neural Information Processing Systems, 2012, 60(2):1097-1105
[15] Zorzi M, Testolin A, Stoianov I P. Modeling language and cognition with deep unsupervised learning:a tutorial overview[J]. Frontiers in Psychology, 2013, 4(5):515-528.
[16] Hinton G E, Osindero S, Teh Y W. A fast learning algorithm for deep belief nets[J]. Neural Computer, 2006, 18(7):1527-1554.
[17] Ranzato M, Krizhevsky A, Hinton G E. Factored 3-way restricted Boltzmann machines for modeling natural images[J]. Journal of Machine Learning Research, 2010(9):621-628.
[18] Hinton G E. Training products of experts by minimizing contrastive divergence[J]. Neural Computer, 2002(148):1771-1800.
[19] Shvachko K, Kuang H, Radia S. The Hadoop distributed fle system[C]//IEEE Symposium on MASS Storage Systems and Technologies, 2010:1-10.
[20] Vemula S, Crick C. Hadoop image processing framework[C]//IEEE International Congress on Big Data, 2015:506-513.
[21] Kachris C, Sirakoulis G C, Soudris D. A map reduce scratchpad memory for multi-core cloud computing applications[J]. Microprocessors & Microsystems, 2015, 39(8):599-608.

Options
Outlines

/

〈 〉