Table of Contents Author Guidelines Submit a Manuscript
Advances in Multimedia
Volume 2016, Article ID 4985313, 10 pages
http://dx.doi.org/10.1155/2016/4985313
Research Article

Classification of Error-Diffused Halftone Images Based on Spectral Regression Kernel Discriminant Analysis

1College of Computer and Communication, Hunan University of Technology, Hunan 412007, China
2Intelligent Information Perception and Processing Technology, Hunan Province Key Laboratory, Hunan 412007, China
3Department of Computer Science, China University of Geosciences, Wuhan, Hubei 430074, China

Received 21 January 2016; Revised 22 March 2016; Accepted 18 April 2016

Academic Editor: Stefanos Kollias

Copyright © 2016 Zhigao Zeng 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.-M. Kwon, M.-G. Kim, and J.-L. Kim, “Multiscale rank-based ordered dither algorithm for digital halftoning,” Information Systems, vol. 48, pp. 241–247, 2015. View at Publisher · View at Google Scholar · View at Scopus
  2. Y. Jiang and M. Wang, “Image fusion using multiscale edge-preserving decomposition based on weighted least squares filter,” IET Image Processing, vol. 8, no. 3, pp. 183–190, 2014. View at Publisher · View at Google Scholar · View at Scopus
  3. Z. Zhao, L. Cheng, and G. Cheng, “Neighbourhood weighted fuzzy c-means clustering algorithm for image segmentation,” IET Image Processing, vol. 8, no. 3, pp. 150–161, 2014. View at Publisher · View at Google Scholar
  4. Z.-Q. Wen, Y.-L. Lu, Z.-G. Zeng, W.-Q. Zhu, and J.-H. Ai, “Optimizing template for lookup-table inverse halftoning using elitist genetic algorithm,” IEEE Signal Processing Letters, vol. 22, no. 1, pp. 71–75, 2015. View at Publisher · View at Google Scholar · View at Scopus
  5. P. C. Chang and C. S. Yu, “Neural net classification and LMS reconstruction to halftone images,” in Visual Communications and Image Processing '98, vol. 3309 of Proceedings of SPIE, pp. 592–602, The International Society for Optical Engineering, January 1998. View at Publisher · View at Google Scholar
  6. Y. Kong, P. Zeng, and Y. Zhang, “Classification and recognition algorithm for the halftone image,” Journal of Xidian University, vol. 38, no. 5, pp. 62–69, 2011 (Chinese). View at Google Scholar
  7. Y. Kong, A study of inverse halftoning and quality assessment schemes [Ph.D. thesis], School of Computer Science and Technology, Xidian University, Xian, China, 2008.
  8. Y.-F. Liu, J.-M. Guo, and J.-D. Lee, “Inverse halftoning based on the Bayesian theorem,” IEEE Transactions on Image Processing, vol. 20, no. 4, pp. 1077–1084, 2011. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  9. Y.-F. Liu, J.-M. Guo, and J.-D. Lee, “Halftone image classification using LMS algorithm and naive Bayes,” IEEE Transactions on Image Processing, vol. 20, no. 10, pp. 2837–2847, 2011. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  10. D. L. Lau and G. R. Arce, Modern Digital Halftoning, CRC Press, Boca Raton, Fla, USA, 2nd edition, 2008.
  11. Image Dithering: Eleven Algorithms and Source Code, http://www.tannerhelland.com/4660/dithering-elevenalgorithms-source-code/.
  12. R. A. Ulichney, “Dithering with blue noise,” Proceedings of the IEEE, vol. 76, no. 1, pp. 56–79, 1988. View at Publisher · View at Google Scholar · View at Scopus
  13. Y.-H. Fung and Y.-H. Chan, “Embedding halftones of different resolutions in a full-scale halftone,” IEEE Signal Processing Letters, vol. 13, no. 3, pp. 153–156, 2006. View at Publisher · View at Google Scholar · View at Scopus
  14. Z.-Q. Wen, Y.-X. Hu, and W.-Q. Zhu, “A novel classification method of halftone image via statistics matrices,” IEEE Transactions on Image Processing, vol. 23, no. 11, pp. 4724–4736, 2014. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  15. D. Cai, X. He, and J. Han, “Efficient kernel discriminant analysis via spectral regression,” in Proceedings of the 7th IEEE International Conference on Data Mining (ICDM '07), pp. 427–432, Omaha, Neb, USA, October 2007. View at Publisher · View at Google Scholar · View at Scopus
  16. D. Cai, X. He, and J. Han, “Speed up kernel discriminant analysis,” The International Journal on Very Large Data Bases, vol. 20, no. 1, pp. 21–33, 2011. View at Publisher · View at Google Scholar · View at Scopus
  17. M. Zhao, Z. Zhang, T. W. S. Chow, and B. Li, “A general soft label based Linear Discriminant Analysis for semi-supervised dimensionality reduction,” Neural Networks, vol. 55, pp. 83–97, 2014. View at Publisher · View at Google Scholar · View at Scopus
  18. M. Zhao, Z. Zhang, T. W. S. Chow, and B. Li, “Soft label based Linear Discriminant Analysis for image recognition and retrieval,” Computer Vision & Image Understanding, vol. 121, no. 1, pp. 86–99, 2014. View at Publisher · View at Google Scholar
  19. L. Zhang and F.-C. Tian, “A new kernel discriminant analysis framework for electronic nose recognition,” Analytica Chimica Acta, vol. 816, pp. 8–17, 2014. View at Publisher · View at Google Scholar · View at Scopus
  20. B. Gu, V. S. Sheng, K. Y. Tay, W. Romano, and S. Li, “Incremental support vector learning for ordinal regression,” IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 7, pp. 1403–1416, 2015. View at Publisher · View at Google Scholar · View at Scopus