A Large Size Image Classification Method Based on Semi-supervised Learning

Author(s): Dan Luo*, Xili Wang

Journal Name: Recent Advances in Electrical & Electronic Engineering
Formerly Recent Patents on Electrical & Electronic Engineering

Volume 13 , Issue 5 , 2020


DOI : 10.2174/1874476105666190830110150

  Journal Home
Become EABM
Become Reviewer
Call for Editor

Graphical Abstract:


Abstract:

Background: Semi-supervised learning in the machine learning community has received widespread attention. Semi-supervised learning can use a small number of tagged samples and a large number of untagged samples for efficient learning.

Methods: In 2014, Kim proposed a new semi-supervised learning method: the minimax label propagation (MMLP) method. This method reduces time complexity to O (n), with a smaller computation cost and stronger classification ability than traditional methods. However, classification results are not accurate in large-scale image classifications. Thus, in this paper, we propose a semisupervised image classification method, which is an MMLP-based algorithm. The main idea is threefold: (1) Improving connectivity of image pixels by pixel sampling to reduce the image size, at the same time, reduce the diversity of image characteristics; (2) Using a recall feature to improve the MMLP algorithm; (3) through classification mapping, gaining the classification of the original data from the classification of the data reduction.

Results: In the end, our algorithm also gains a minimax path from untagged samples to tagged samples. The experimental results proved that this algorithm is applicable to semi-supervised learning on small-size and that it can also gain better classification results for large-size image at the same time.

Conclusion: In our paper, considering the connectivity of the neighboring matrix and the diversity of the characteristics, we used meanshift clustering algorithm, next we will use fuzzy energy clustering on our algorithm. We will study the function of these paths.

Keywords: Graph-based semi-supervised learning, MMLP algorithm, data reduction, recall feature, classification mapping, neural network models.

[1]
T. Enmei, and J. Yang, "Semi-supervised learning methods", vol. 38., Chin. J. Comput.. 2015
[2]
Y. Luo, D. Tao, B. Geng, C. Xu, and S.J. Maybank, "Manifold regularized multitask learning for semi-supervised multilabel image classification", IEEE Trans. Image Process., vol. 22, no. 2, pp. 523-536, 2013.
[http://dx.doi.org/10.1109/TIP.2012.2218825] [PMID: 22997267]
[3]
T. Enmei, and Y. Jia, "Review of semi -supervised learning theories and recent advances", J. Shanghai Jiaotong Univ., vol. 5, no. 10, 2018.
[4]
O. Chapelle and, B. Scholkopf, and A. Zien, Semi-SupervisedLearning., MITPress: Cambridge, USA, 2006.
[http://dx.doi.org/10.7551/mitpress/9780262033589.001.0001]
[5]
X. 5-Luo228-MS.docxZhu, , "Semi-supervised learning literature survey", Department of Computer Science, University of Wisconsin- Madison, Wisconsin: Technical Report,. 2006
[6]
Z.H. Zhou, and M. Li, "Semi-supervised learning by disagreement", Knowl. Inf. Syst., vol. 24, pp. 415-439, 2010.
[7]
W. Wang, and Z.G. Zhou, "Analyzing co-training style algorithms", Machine learning: ECML 2007", In: , 18th European Conference on Machine Learning Warsaw, Poland 2007
[8]
A. Blum, and S. Chawla, "Learning from labeled and unlabeled data using graph mincuts", In: ; Proceedings of the 18th International Conference on Machine Learning Williams College: USA , 2001, pp. 19-26.
[9]
K.H. Kim, and S.J. Choi, "Label propagation through minimax paths for scalable semi-supervised learning", Pattern Recognit. Lett., vol. 45, pp. 17-25, 2014.
[http://dx.doi.org/10.1016/j.patrec.2014.02.020]
[10]
X.J. Zhu, and J. Lafferty, "Harmonic mixtures: Combining mixture models and graph-based methods for inductive and scalable semi-supervised learning", In: ; Proceedings of the 22th International Conference on Machine Learning Bonn, Germany, 2005, pp.1052-1059
[11]
D. Zhou, O. Bousquen, T.N. Lal, J. Weston, and B. Scholkoph, "Learning with local and global consistency", In: , Proceedings of the Conference on Neural Information Processing Systems, Cambridge, USA: MIT Perss, pp. 321-328, 2004.
[12]
M. Belkin, P. Niyogi, and V. Sindhwani, "Manifold Regularization: a geometric framework for learning from labeled and unlabeled examples", J. Mach. Learn. Res., vol. 7, pp. 2399-2434, 2006.
[13]
J. Zhang, Y. Chang, and S. Wen, "The overview on label propagation algorithm and Applications", Jisuanji Yingyong Yanjiu, vol. 30, no. 1, pp. 21-25, 2013.
[14]
X. Wang, and L. Hong-Shuai, "Label propagation through minimum cost path", Chinese J. Comput.,, vol. 39, , no. 7,, . 2016
[15]
X. Chen, G. Yu, Q. Tan, and J. Wang, "Weighted samples based semi-supervised classification", Appl. Soft Comput., vol. 79, no. June, pp. 46-58, 2019.
[16]
F. Nie, G. Cai, Jing Li, and Xuelong Li, "Auto-weighted multi-view learning for image clustering and semi-supervised classification", IEEE Trans. Image Process., vol. 27, no. 3, pp. 1501-1511, 2018.
[http://dx.doi.org/10.1109/TIP.2017.2754939] [PMID: 28945592]
[17]
R. Liao, M. Brockschmidt, and D. Tarlow, "Graph partition neural networks for semi-supervised classification" In: , arXiv preprint arXiv. . pp. 1803, 2018
[18]
Q. Cheng, H. Zhou, J. Cheng, and H. Li, "A minimax framework for classification with applications to images and high dimensional data", IEEE Trans. Pattern Anal. Mach. Intell., vol. 36, no. 11, pp. 2117-2130, 2014.
[http://dx.doi.org/10.1109/TPAMI.2014.2327978] [PMID: 26353055]
[19]
C. Peng, J. Cheng, and Q. Cheng, "A supervised learning model for high-dimensional and Large-Scale Data", ACM Trans. Intell. Syst. Technol., vol. 8, no. 2, 2016.
[http://dx.doi.org/10.1145/2972957]
[20]
L. Gong, and Q. Cheng, "Exploiting edge features in graph neural networks", Comput. Sci., Mach. Learn., . Available at: arXiv:1809.02709 .
[21]
Y. Luo, D. Tao, C. Xu, C. Xu, H. Liu, and Y. Wen, "Multiview vector-valued manifold regularization for multilabel image classification", IEEE Trans. Neural Netw. Learn. Syst., vol. 24, no. 5, pp. 709-722, 2013.
[http://dx.doi.org/10.1109/TNNLS.2013.2238682] [PMID: 24808422]
[22]
O. Chapelle, J. Weston, and B. Schölkopf, Cluster kernels for semi-supervised learning.Advances in Neural Information Processing Systems (NIPS)., 2003.
[23]
J. Tang, R. Hong, S. Yan, T.S. Chua, and G.J. Qi, "Jain Image annotation by kNN-sparse graph-based label propagation over noisily-tagged web images ACM Trans", Intell. Syst. Technol., vol. 2, no. 2, pp. 1-15, 2011.
[http://dx.doi.org/10.1145/1899412.1899418]
[24]
W. Bao-Yun, and F. Bao-Jie, "Adoptive mean shift tracking algorithm based on the com-bined feature histogram of color and texture", J. Nanjing Uni. Posts and Telecommunications (Natural Sience)..
[25]
E. Borenstein, Weizmann horse database..Available at: , http://www.msri.org/people/members/eranb/
[26]
W.Z. Qiang, and C. Xing, "Errors of bhattacharyya coefficient and its reduction in object tracking", Chin. J. Computers, , vol. 31. , . 2008, no. 7, pp. 1165-1173.
[27]
D. Luo, "A variational level set remote sensing image segmentation algorithm that based on fuzzy energy clustering", Electron. Optics Cont., vol. 22,, no. 8,, . 2015


Rights & PermissionsPrintExport Cite as

Article Details

VOLUME: 13
ISSUE: 5
Year: 2020
Published on: 21 September, 2020
Page: [669 - 680]
Pages: 12
DOI: 10.2174/1874476105666190830110150
Price: $25

Article Metrics

PDF: 14
HTML: 2



Most Downloaded Article(s)