Table of Contents Author Guidelines Submit a Manuscript
Journal of Applied Mathematics
Volume 2014, Article ID 898705, 10 pages
http://dx.doi.org/10.1155/2014/898705
Research Article

Building Recognition on Subregion’s Multiscale Gist Feature Extraction and Corresponding Columns Information Based Dimensionality Reduction

1College of Computer Science and Technology, Jilin University, Changchun 130012, China
2School of Natural and Computing Sciences, University of Aberdeen, Aberdeen AB24 3UE, UK

Received 24 October 2013; Accepted 3 April 2014; Published 27 April 2014

Academic Editor: Huijun Gao

Copyright © 2014 Bin Li 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. M. M. Ullah, A. Pronobis, B. Caputo, J. Luo, P. Jensfelt, and H. I. Christensen, “Towards robust place recognition for robot localization,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA '08), pp. 530–537, May 2008. View at Publisher · View at Google Scholar · View at Scopus
  2. H. Ali, G. Paar, and L. Paletta, “Semantic Indexing for visual recognition of buildings,” in Proceedings of the International Symposium on Mobile Mapping Technology, pp. 28–31, 2007.
  3. R. Hutchings and W. Mayol-Cuevas, “Building recognition for mobile devices: incorporating positional information with visual features,” Tech. Rep. CSTR-06-017, Computer Science, University of Bristol, 2005. View at Google Scholar
  4. Y. Li and L. G. Shapiro, “Consistent line clusters for building recognition in CBIR,” in Proceedings of the IEEE International Conference on Pattern Recognition, vol. 3, pp. 952–956, 2002.
  5. W. Zhang and J. Košecká, “Hierarchical building recognition,” Image and Vision Computing, vol. 25, no. 5, pp. 704–716, 2007. View at Publisher · View at Google Scholar · View at Scopus
  6. J. Li and N. M. Allinson, “Subspace learning-based dimensionality reduction in building recognition,” Neurocomputing, vol. 73, no. 1-3, pp. 324–330, 2009. View at Publisher · View at Google Scholar · View at Scopus
  7. C. Siagian and L. Itti, “Rapid biologically-inspired scene classification using features shared with visual attention,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 2, pp. 300–312, 2007. View at Publisher · View at Google Scholar · View at Scopus
  8. I. T. Jolliffe, Principal Component Analysis, Springer, New York, NY, USA, 1989. View at MathSciNet
  9. X. F. He and P. Niyogi, “Locality preserving projections,” in Proceedings of the Neural Information Processing Systems (NIPS '04), vol. 6, pp. 1059–1071, 2004.
  10. G. J. McLachlan, Discriminant Analysis and Statistical Pattern Recognition, John Wiley & Sons, New York, NY, USA, 2004.
  11. H. Yu and J. Yang, “A direct LDA algorithm for high-dimensional data—with application to face recognition,” Pattern Recognition, vol. 34, pp. 2067–2070, 2001. View at Publisher · View at Google Scholar
  12. A. M. Martinez and A. C. Kak, “PCA versus LDA,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 2, pp. 228–233, 2001. View at Publisher · View at Google Scholar · View at Scopus
  13. P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, “Eigenfaces vs. fisherfaces: recognition using class specific linear projection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711–720, 1997. View at Publisher · View at Google Scholar · View at Scopus
  14. X. He, D. Cai, S. Yan, and H.-J. Zhang, “Neighborhood preserving embedding,” in Proceedings of the 10th IEEE International Conference on Computer Vision (ICCV '05), vol. 2, pp. 1208–1213, October 2005. View at Scopus
  15. S. Yin, S. Ding, A. Haghani, H. Hao, and P. Zhang, “A comparison study of basic datadriven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process,” Journal of Process Control, vol. 22, pp. 1567–1581, 2012. View at Publisher · View at Google Scholar
  16. S. Yin, G. Wang, and H. Karimi, “Data-driven design of robust fault detection system for wind turbines,” Mechatronics, 2013. View at Publisher · View at Google Scholar
  17. S. Yin, S. X. Ding, A. H. A. Sari, and H. Hao, “Data-driven monitoring for stochastic systems and its application on batch process,” International Journal of Systems Science, vol. 44, no. 7, pp. 1366–1376, 2013. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  18. S. Yin, H. Luo, and S. Ding, “Real-time implementation of fault-tolerant control systems with performance optimization,” IEEE Transactions on Industrial Electronics, vol. 64, pp. 2402–2411, 2014. View at Google Scholar
  19. X. Zhao, L. Zhang, P. Shi, and H. R. Karimi, “Robust control of continuous-time systems with state-dependent uncertainties and its application to electronic circuits,” IEEE Transactions on Industrial Electronics, vol. 61, pp. 4161–4170, 2014. View at Publisher · View at Google Scholar
  20. X. Zhao, H. Liu, and J. Zhang, “Multiple-mode observer design for a class of switched linear systems linear systems,” IEEE Transactions on Automation Science and Engineering, 2013. View at Publisher · View at Google Scholar
  21. J. B. Tenenbaum, V. De Silva, and J. C. Langford, “A global geometric framework for nonlinear dimensionality reduction,” Science, vol. 290, no. 5500, pp. 2319–2323, 2000. View at Publisher · View at Google Scholar · View at Scopus
  22. K. Q. Weinberger, F. Sha, and L. K. Saul, “Learning a kernel matrix for nonlinear dimensionality reduction,” in Proceedings of the 21st International Conference on Machine Learning (ICML '04), p. 106, ACM, July 2004. View at Scopus
  23. B. Yang and S. Chen, “Sample-dependent graph construction with application to dimensionality reduction,” Neurocomputing, vol. 74, no. 1–3, pp. 301–314, 2010. View at Publisher · View at Google Scholar · View at Scopus
  24. A. Oliva and A. Torralba, “Modeling the shape of the scene: a holistic representation of the spatial envelope,” International Journal of Computer Vision, vol. 42, no. 3, pp. 145–175, 2001. View at Publisher · View at Google Scholar · View at Scopus
  25. L. Itti, C. Koch, and E. Niebur, “A model of saliency-based visual attention for rapid scene analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 11, pp. 1254–1259, 1998. View at Publisher · View at Google Scholar · View at Scopus
  26. B. Leibe and B. Schiele, “Interleaved object categorization and segmentation,” in Proceedings of the British Machine Vision Conference (BMVC '03), pp. 759–768, 2003.
  27. S. Yan, D. Xu, B. Zhang, H.-J. Zhang, Q. Yang, and S. Lin, “Graph embedding and extensions: a general framework for dimensionality reduction,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 1, pp. 40–51, 2007. View at Publisher · View at Google Scholar · View at Scopus
  28. M. A. Turk and A. P. Pentland, “Face recognition using eigenfaces,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 586–591, June 1991. View at Scopus
  29. http://eeepro.shef.ac.uk/building/dataset.rar.