Table of Contents Author Guidelines Submit a Manuscript
Evidence-Based Complementary and Alternative Medicine
Volume 2017, Article ID 7452427, 12 pages
https://doi.org/10.1155/2017/7452427
Research Article

Tongue Images Classification Based on Constrained High Dispersal Network

1MOE Research Center for Software/Hardware Co-Design Engineering, East China Normal University, Shanghai 200062, China
2Department of Computer Science, University of Missouri, Columbia, MO 65211, USA
3Department of TCM Information and Technology Center, Shanghai University of TCM, Shanghai, China
4Department of Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong New Area, Shanghai 201203, China

Correspondence should be addressed to Guitao Cao; nc.ude.unce.ies@oactg and Minghua Zhu; nc.ude.unce.ies@uhzhm

Received 31 August 2016; Revised 5 January 2017; Accepted 17 January 2017; Published 30 March 2017

Academic Editor: Jeng-Ren Duann

Copyright © 2017 Dan Meng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. Y. Jiao, X. Zhang, L. Zhuo, M. Chen, and K. Wang, “Tongue image classification based on Universum SVM,” in Proceedings of the 3rd International Conference on BioMedical Engineering and Informatics (BMEI '10), pp. 657–660, IEEE, Yantai, China, October 2010. View at Publisher · View at Google Scholar · View at Scopus
  2. B. Zhang, X. Wang, J. You, and D. Zhang, “Tongue color analysis for medical application,” Evidence-based Complementary and Alternative Medicine, vol. 2013, Article ID 264742, 11 pages, 2013. View at Publisher · View at Google Scholar · View at Scopus
  3. T. Obafemi-Ajayi, R. Kanawong, D. Xu, S. Li, and Y. Duan, “Features for automated tongue image shape classification,” in Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW '12), pp. 273–279, October 2012. View at Publisher · View at Google Scholar · View at Scopus
  4. C. H. Li and P. C. Yuen, “Tongue image matching using color content,” Pattern Recognition, vol. 35, no. 2, pp. 407–419, 2002. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  5. Y.-G. Wang, J. Yang, Y. Zhou, and Y.-Z. Wang, “Region partition and feature matching based color recognition of tongue image,” Pattern Recognition Letters, vol. 28, no. 1, pp. 11–19, 2007. View at Publisher · View at Google Scholar · View at Scopus
  6. Q. Li and Z. Liu, “Tongue color analysis and discrimination based on hyperspectral images,” Computerized Medical Imaging and Graphics, vol. 33, no. 3, pp. 217–221, 2009. View at Publisher · View at Google Scholar · View at Scopus
  7. X. Wang, B. Zhang, Z. Yang, H. Wang, and D. Zhang, “Statistical analysis of tongue images for feature extraction and diagnostics,” IEEE Transactions on Image Processing, vol. 22, no. 12, pp. 5336–5347, 2013. View at Publisher · View at Google Scholar · View at Scopus
  8. B. Zhang and H. Zhang, “Significant geometry features in tongue image analysis,” Evidence-Based Complementary and Alternative Medicine, vol. 2015, Article ID 897580, 8 pages, 2015. View at Publisher · View at Google Scholar · View at Scopus
  9. G. Cao, J. Ding, Y. Duan, L. Tu, J. Xu, and D. Xu, “Classification of tongue images based on doublet and color space dictionary,” in Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM '16), Shenzhen, China, December 2016. View at Publisher · View at Google Scholar
  10. L. Zhang, L. Yang, and T. Luo, “Unified saliency detection model using color and texture features,” PLoS ONE, vol. 11, no. 2, Article ID e0149328, 2016. View at Publisher · View at Google Scholar · View at Scopus
  11. L. Zhang, Y. Sun, T. Luo, and M. M. Rahman, “Note: a manifold ranking based saliency detection method for camera,” Review of Scientific Instruments, vol. 87, no. 9, Article ID 096103, 2016. View at Publisher · View at Google Scholar
  12. R. Kanawong, T. Obafemi-Ajayi, J. Yu, D. Xu, S. Li, and Y. Duan, “ZHENG classification in Traditional Chinese Medicine based on modified specular-free tongue images,” in Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW '12), pp. 288–294, IEEE, Philadelphia, Pa, USA, October 2012. View at Publisher · View at Google Scholar · View at Scopus
  13. Z. Guo, “Tongue image matching using color and texture,” in Proceedings of the International Conference on Medical Biometrics (ICMB '08), pp. 273–281, Hong Kong, 2008.
  14. B. Pang, D. Zhang, and K. Wang, “Tongue image analysis for appendicitis diagnosis,” Information Sciences, vol. 175, no. 3, pp. 160–176, 2005. View at Publisher · View at Google Scholar · View at Scopus
  15. X. Zhang, J. Zhang, G. Hu, and Y. Wang, “Preliminary study of tongue image classification based on multi-label learning,” in Advanced Intelligent Computing Theories and Applications, vol. 9227 of Lecture Notes in Computer Science, pp. 208–220, Springer, 2015. View at Google Scholar
  16. Y. Bengio, A. Courville, and P. Vincent, “Representation learning: a review and new perspectives,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 8, pp. 1798–1828, 2013. View at Publisher · View at Google Scholar · View at Scopus
  17. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in Proceedings of the 26th Annual Conference on Neural Information Processing Systems (NIPS '12), December 2012. View at Scopus
  18. Y. Taigman, M. Yang, M. Ranzato, and L. Wolf, “DeepFace: closing the gap to human-level performance in face verification,” in Proceedings of the 27th IEEE Conference on Computer Vision and Pattern Recognition (CVPR '14), pp. 1701–1708, June 2014. View at Publisher · View at Google Scholar · View at Scopus
  19. D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision, vol. 60, no. 2, pp. 91–110, 2004. View at Publisher · View at Google Scholar · View at Scopus
  20. N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '05), pp. 886–893, IEEE, San Diego, Calif, USA, June 2005. View at Publisher · View at Google Scholar · View at Scopus
  21. F. Wang, W. Zuo, L. Zhang, D. Meng, and D. Zhang, “A kernel classification framework for metric learning,” IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 9, pp. 1950–1962, 2015. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  22. J. Ding, G. Cao, and D. Meng, “Classification of tongue images based on doublet SVM,” in Proceedings of the International Symposium on System and Software Reliability (ISSSR '06), pp. 77–81, Shanghai, China, October 2016. View at Publisher · View at Google Scholar
  23. T.-H. Chan, K. Jia, S. Gao, J. Lu, Z. Zeng, and Y. Ma, “PCANet: a simple deep learning baseline for image classification?” IEEE Transactions on Image Processing, vol. 24, no. 12, pp. 5017–5032, 2015. View at Publisher · View at Google Scholar · View at Scopus
  24. G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. R. Salakhutdinov, “Improving neural networks by preventing co-adaptation of feature detectors,” Computing Science, vol. 3, no. 4, pp. 212–223, 2012. View at Google Scholar
  25. J. W. Huang and C. Yuan, “Weighted-PCANet for face recognition,” in Neural Information Processing, vol. 9492 of Lecture Notes in Computer Science, pp. 246–254, Springer, Berlin, Germany, 2015. View at Publisher · View at Google Scholar
  26. S. Wang, L. Chen, Z. Zhou, X. Sun, and J. Dong, “Human fall detection in surveillance video based on PCANet,” Multimedia Tools and Applications, vol. 75, no. 19, pp. 11603–11613, 2015. View at Publisher · View at Google Scholar · View at Scopus
  27. Z. Huang, W. Xue, Q. Mao, and Y. Zhan, “Unsupervised domain adaptation for speech emotion recognition using PCANet,” Multimedia Tools and Applications, pp. 1–15, 2016. View at Publisher · View at Google Scholar · View at Scopus
  28. T. Ojala, M. Pietikäinen, and T. Mäenpää, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 971–987, 2002. View at Publisher · View at Google Scholar · View at Scopus
  29. R. Kanawong, T. Obafemi-Ajayi, T. Ma, D. Xu, S. Li, and Y. Duan, “Automated tongue feature extraction for ZHENG classification in Traditional Chinese Medicine,” Evidence-Based Complementary and Alternative Medicine, vol. 2012, Article ID 912852, 14 pages, 2012. View at Publisher · View at Google Scholar · View at Scopus
  30. R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin, “LIBLINEAR: a library for large linear classification,” Journal of Machine Learning Research, vol. 9, pp. 1871–1874, 2008. View at Google Scholar · View at Scopus
  31. V. N. Vapnik, Statistical Learning Theory, Adaptive and Learning Systems for Signal Processing, Communications, and Control, John Wiley & Sons, Inc., New York, NY, USA, 1998. View at MathSciNet
  32. F. Shen, C. Shen, X. Zhou, Y. Yang, and H. T. Shen, “Face image classification by pooling raw features,” Pattern Recognition, vol. 54, pp. 94–103, 2014. View at Publisher · View at Google Scholar · View at Scopus
  33. C. C. Chang and C. J. Lin, “LIBSVM: a library for support vector machines,” ACM Transactions on Intelligent Systems and Technology, vol. 2, no. 3, pp. 389–396, 2011. View at Google Scholar