Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2015, Article ID 109718, 14 pages
http://dx.doi.org/10.1155/2015/109718
Research Article

A New Scene Classification Method Based on Local Gabor Features

1College of Electric Information, Dalian Jiaotong University, Dalian 116028, China
2Marine Engineering College, Dalian Maritime University, Dalian 116026, China

Received 10 November 2014; Revised 9 March 2015; Accepted 6 April 2015

Academic Editor: Lucian Busoniu

Copyright © 2015 Baoyu Dong and Guang Ren. 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. X. Zhou, X. D. Zhuang, H. Tang, M. Hasegawa-Johnson, and T. S. Huang, “Novel Gaussianized vector representation for improved natural scene categorization,” Pattern Recognition Letters, vol. 31, no. 8, pp. 702–708, 2010. View at Publisher · View at Google Scholar · View at Scopus
  2. A. Vailaya, M. A. T. Figueiredo, A. K. Jain, and H.-J. Zhang, “Image classification for content-based indexing,” IEEE Transactions on Image Processing, vol. 10, no. 1, pp. 117–130, 2001. View at Publisher · View at Google Scholar · View at Scopus
  3. N. Serrano, A. E. Savakis, and J. Luo, “Improved scene classification using efficient low-level features and semantic cues,” Pattern Recognition, vol. 37, no. 9, pp. 1773–1784, 2004. View at Publisher · View at Google Scholar · View at Scopus
  4. F.-F. Li and P. Perona, “A bayesian hierarchical model for learning natural scene categories,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '05), pp. 524–531, June 2005. View at Scopus
  5. P. Quelhas, F. Monay, J.-M. Odobez, D. Gatica-Perez, and T. Tuytelaars, “A thousand words in a scene,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 9, pp. 1575–1589, 2007. View at Publisher · View at Google Scholar · View at Scopus
  6. L. Nanni, A. Lumini, and S. Brahnam, “Ensemble of different local descriptors, codebook generation methods and subwindow configurations for building a reliable computer vision system,” Journal of King Saud University—Science, vol. 26, no. 2, pp. 89–100, 2014. View at Publisher · View at Google Scholar · View at Scopus
  7. Z. Li and K.-H. Yap, “An efficient approach for scene categorization based on discriminative codebook learning in bag-of-words framework,” Image and Vision Computing, vol. 31, no. 10, pp. 748–755, 2013. View at Publisher · View at Google Scholar · View at Scopus
  8. J. Qin and N. H. C. Yung, “Scene categorization via contextual visual words,” Pattern Recognition, vol. 43, no. 5, pp. 1874–1888, 2010. View at Publisher · View at Google Scholar · View at Scopus
  9. N. M. Elfiky, J. Gonzàlez, and F. X. Roca, “Compact and adaptive spatial pyramids for scene recognition,” Image and Vision Computing, vol. 30, no. 8, pp. 492–500, 2012. View at Publisher · View at Google Scholar · View at Scopus
  10. L. Zhou, Z. T. Zhou, and D. W. Hu, “Scene classification using a multi-resolution bag-of-features model,” Pattern Recognition, vol. 46, no. 1, pp. 424–433, 2013. View at Publisher · View at Google Scholar · View at Scopus
  11. S. Lazebnik, C. Schmid, and J. Ponce, “Beyond bags of features: spatial pyramid matching for recognizing natural scene categories,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2169–2178, June 2006. View at Publisher · View at Google Scholar · View at Scopus
  12. A. Oliva and A. Torralba, “Chapter 2 Building the gist of a scene: the role of global image features in recognition,” Progress in Brain Research, vol. 155, pp. 23–36, 2006. View at Publisher · View at Google Scholar · View at Scopus
  13. F. F. Li, R. VanRullen, C. Koch, and P. Perona, “Rapid natural scene categorization in the near absence of attention,” Proceedings of the National Academy of Sciences of the United States of America, vol. 99, no. 14, pp. 9596–9601, 2002. View at Publisher · View at Google Scholar · View at Scopus
  14. 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
  15. K. Hotta, “Local co-occurrence features in subspace obtained by KPCA of local blob visual words for scene classification,” Pattern Recognition, vol. 45, no. 10, pp. 3687–3694, 2012. View at Publisher · View at Google Scholar · View at Scopus
  16. K. Hotta, “Local autocorrelation of similarities with subspaces for shift invariant scene classification,” Pattern Recognition, vol. 44, no. 4, pp. 794–799, 2011. View at Publisher · View at Google Scholar · View at Scopus
  17. A. Bolovinou, I. Pratikakis, and S. Perantonis, “Bag of spatio-visual words for context inference in scene classification,” Pattern Recognition, vol. 46, no. 3, pp. 1039–1053, 2013. View at Publisher · View at Google Scholar · View at Scopus
  18. L. Nanni and A. Lumini, “Heterogeneous bag-of-features for object/scene recognition,” Applied Soft Computing Journal, vol. 13, no. 4, pp. 2171–2178, 2013. View at Publisher · View at Google Scholar · View at Scopus
  19. X. L. Meng, Z. Z. Wang, and L. Z. Wu, “Building global image features for scene recognition,” Pattern Recognition, vol. 45, no. 1, pp. 373–380, 2012. View at Publisher · View at Google Scholar · View at Scopus
  20. J. Li, X. Li, and D. Tao, “KPCA for semantic object extraction in images,” Pattern Recognition, vol. 41, no. 10, pp. 3244–3250, 2008. View at Publisher · View at Google Scholar · View at Scopus
  21. P. F. Jia, F. C. Tian, Q. H. He, S. Fan, J. L. Liu, and S. X. Yang, “Feature extraction of wound infection data for electronic nose based on a novel weighted KPCA,” Sensors and Actuators B: Chemical, vol. 201, pp. 555–566, 2014. View at Publisher · View at Google Scholar
  22. Y. W. Zhang, “Enhanced statistical analysis of nonlinear processes using KPCA, KICA and SVM,” Chemical Engineering Science, vol. 64, no. 5, pp. 801–811, 2009. View at Publisher · View at Google Scholar · View at Scopus
  23. M. X. Jia, H. Y. Xu, X. F. Liu, and N. Wang, “The optimization of the kind and parameters of kernel function in KPCA for process monitoring,” Computers and Chemical Engineering, vol. 46, pp. 94–104, 2012. View at Publisher · View at Google Scholar · View at Scopus
  24. Y. Xu, D. Zhang, F. Song, J.-Y. Yang, Z. Jing, and M. Li, “A method for speeding up feature extraction based on KPCA,” Neurocomputing, vol. 70, no. 4–6, pp. 1056–1061, 2007. View at Publisher · View at Google Scholar · View at Scopus
  25. C.-W. Hsu and C.-J. Lin, “A comparison of methods for multiclass support vector machines,” IEEE Transactions on Neural Networks, vol. 13, no. 2, pp. 415–425, 2002. View at Publisher · View at Google Scholar · View at Scopus