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Computational Intelligence and Neuroscience
Volume 2017 (2017), Article ID 5169675, 14 pages
https://doi.org/10.1155/2017/5169675
Research Article

Bag of Visual Words Model with Deep Spatial Features for Geographical Scene Classification

College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Correspondence should be addressed to Yuanyuan Liu

Received 13 February 2017; Revised 31 March 2017; Accepted 18 May 2017; Published 19 June 2017

Academic Editor: Athanasios Voulodimos

Copyright © 2017 Jiangfan Feng 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. G. Csurka, C. R. Dance, L. Fan, J. Willamowski, and C. Bray, “Visual categorization with bags of keypoints,” Workshop on Statistical Learning in Computer Vision Eccv, pp. 1–22, 2004. View at Google Scholar
  2. D. Song and D. Tao, “Biologically inspired feature manifold for scene classification,” IEEE Transactions on Image Processing, vol. 19, no. 1, pp. 174–184, 2010. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  3. P. Quelhas, F. Monay, J.-M. Odobez, D. Gatica-Perez, T. Tuytelaars, and L. Van Gool, “Modeling scenes with local descriptors and latent aspects,” in Proceedings of the 10th IEEE International Conference on Computer Vision, ICCV 2005, pp. 883–890, October 2005. View at Publisher · View at Google Scholar · View at Scopus
  4. 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 (CVPR '06), pp. 2169–2178, June 2006. View at Publisher · View at Google Scholar · View at Scopus
  5. 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
  6. Y. Zheng, Y. Jiang, and X. Xue, “Learning hybrid part filters for scene recognition,” in Computer Vision—ECCV 2012, vol. 7576 of Lecture Notes in Computer Science, pp. 172–185, Springer, Berlin, Germany, 2012. View at Publisher · View at Google Scholar
  7. J. Sun and J. Ponce, “Learning discriminative part detectors for image classification and cosegmentation,” in Proceedings of the 2013 14th IEEE International Conference on Computer Vision, ICCV 2013, pp. 3400–3407, December 2013. View at Publisher · View at Google Scholar · View at Scopus
  8. K. Fukushima, “Neocognitron: a hierarchical neural network capable of visual pattern recognition,” Neural Networks, vol. 1, no. 2, pp. 119–130, 1988. View at Google Scholar · View at Scopus
  9. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278–2323, 1998. View at Publisher · View at Google Scholar · View at Scopus
  10. D. C. An, U. Meier, and J. Masci, “Flexible, high performance convolutional neural networks for image classification[C]// IJCAI,” in Proceedings of the International Joint Conference on Artificial Intelligence, pp. 1237–1242, Barcelona, Spain, 2011.
  11. P. Y. Simard, D. Steinkraus, and J. C. Platt, “Best practices for convolutional neural networks applied to visual document analysis,” in Proceedings of the 7th International Conference on Document Analysis and Recognition, vol. 2, pp. 958–963, IEEE Computer Society, Edinburgh, UK, August 2003. View at Publisher · View at Google Scholar
  12. C. Szegedy, W. Liu, Y. Jia et al., “Going deeper with convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '15), pp. 1–9, IEEE, Boston, MA, USA, June 2015. View at Publisher · View at Google Scholar
  13. G. Hinton E, N. Srivastava, and A. Krizhevsky, “Improving neural networks by preventing co-adaptation of feature detectors,” Computer Science, vol. 3, no. 4, pp. 212–223, 2012. View at Google Scholar
  14. B. Zhou, A. Garcia L, J. Xiao et al., “Learning deep features for scene recognition using places database,” in Advances in Neural Information Processing Systems (NIPS), vol. 1, pp. 487–495, 2015. View at Google Scholar
  15. A. S. Razavian, H. Azizpour, J. Sullivan, and S. Carlsson, “Cnn features off-the-shelf: an astounding baseline for recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW '14), pp. 512–519, IEEE, Columbus, OH, USA, June 2014. View at Publisher · View at Google Scholar · View at Scopus
  16. F. Hu, G.-S. Xia, J. Hu, and L. Zhang, “Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery,” Remote Sensing, vol. 7, no. 11, pp. 14680–14707, 2015. View at Publisher · View at Google Scholar · View at Scopus
  17. Y. Xu, T. Mo, Q. Feng, P. Zhong, M. Lai, and E. I.-C. Chang, “Deep learning of feature representation with multiple instance learning for medical image analysis,” in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '14), pp. 1626–1630, Florence, France, May 2014. View at Publisher · View at Google Scholar · View at Scopus
  18. Z. Zuo, G. Wang, B. Shuai, L. Zhao, Q. Yang, and X. Jiang, “Learning discriminative and shareable features for scene classification,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8689, no. 1, pp. 552–568, 2014. View at Publisher · View at Google Scholar · View at Scopus
  19. K. Makantasis, K. Karantzalos, A. Doulamis, and N. Doulamis, “Deep supervised learning for hyperspectral data classification through convolutional neural networks,” in Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015, pp. 4959–4962, ita, July 2015. View at Publisher · View at Google Scholar · View at Scopus
  20. W. Hu, Y. Huang, L. Wei, F. Zhang, and H. Li, “Deep convolutional neural networks for hyperspectral image classification,” Journal of Sensors, vol. 2015, no. 2, Article ID 258619, 12 pages, 2015. View at Publisher · View at Google Scholar · View at Scopus
  21. Y. Chen, Z. Lin, X. Zhao, G. Wang, and Y. Gu, “Deep learning-based classification of hyperspectral data,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, no. 6, pp. 2094–2107, 2014. View at Publisher · View at Google Scholar
  22. M. Vakalopoulou, K. Karantzalos, N. Komodakis, and N. Paragios, “Building detection in very high resolution multispectral data with deep learning features,” in Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015, pp. 1873–1876, July 2015. View at Publisher · View at Google Scholar · View at Scopus
  23. A. Merentitis and C. Debes, “Automatic fusion and classification using random forests and features extracted with deep learning,” in Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015, pp. 2943–2946, July 2015. View at Publisher · View at Google Scholar · View at Scopus
  24. M. Cimpoi, S. Maji, and A. Vedaldi, “Deep filter banks for texture recognition and segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, pp. 3828–3836, June 2015. View at Publisher · View at Google Scholar · View at Scopus
  25. Y. Gong, L. Wang, R. Guo, and S. Lazebnik, “Multi-scale orderless pooling of deep convolutional activation features,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8695, no. 7, pp. 392–407, 2014. View at Publisher · View at Google Scholar · View at Scopus
  26. X.-X. Niu and C. Y. Suen, “A novel hybrid CNN-SVM classifier for recognizing handwritten digits,” Pattern Recognition, vol. 45, no. 4, pp. 1318–1325, 2012. View at Publisher · View at Google Scholar · View at Scopus
  27. K. He, X. Zhang, S. Ren, and J. Sun, “Spatial pyramid pooling in deep convolutional networks for visual recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 9, pp. 1904–1916, 2015. View at Publisher · View at Google Scholar · View at Scopus
  28. 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, vol. 2, pp. 524–531, IEEE Computer Society, 2005.
  29. A. Quattoni and A. Torralba, “Recognizing indoor scenes,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops '09), pp. 413–420, June 2009. View at Publisher · View at Google Scholar · View at Scopus
  30. R. Margolin, L. Zelnik-Manor, and A. Tal, “OTC: A novel local descriptor for scene classification,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8695, no. 7, pp. 377–391, 2014. View at Publisher · View at Google Scholar · View at Scopus
  31. F. Sadeghi and M. F. Tappen, “Latent pyramidal regions for recognizing scenes,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7576, no. 5, pp. 228–241, 2012. View at Publisher · View at Google Scholar · View at Scopus
  32. S. Singh, A. Gupta, and A. A. Efros, “Unsupervised discovery of mid-level discriminative patches,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7573, no. 2, pp. 73–86, 2012. View at Publisher · View at Google Scholar · View at Scopus
  33. M. Juneja, A. Vedaldi, C. V. Jawahar, and A. Zisserman, “Blocks that shout: Distinctive parts for scene classification,” in Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013, pp. 923–930, June 2013. View at Publisher · View at Google Scholar · View at Scopus
  34. A. Nguyen, J. Yosinski, and J. Clune, “Deep neural networks are easily fooled: High confidence predictions for unrecognizable images,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, pp. 427–436, June 2015. View at Publisher · View at Google Scholar · View at Scopus
  35. M. Abadi, A. Agarwal, P. Barham, E. Brevdo, and Z. Chen, “TensorFlow: Large-scale machine learning on heterogeneous systems,” 2015, https://www.tensorflow.org/. View at Google Scholar
  36. J. Alonso and Y. Chen, “Receptive field,” Scholarpedia, vol. 4, no. 1, p. 5393, 2009. View at Publisher · View at Google Scholar
  37. A. Mahendran and A. Vedaldi, “Understanding deep image representations by inverting them,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, pp. 5188–5196, June 2015. View at Publisher · View at Google Scholar · View at Scopus
  38. C. Chang, J. Lin C, and LIBSVM., “A library for support vector machines,” in Acm Transactions on Intelligent Systems & Technology, vol. 2, pp. 389–396, 3, article 27 edition, 2007. View at Google Scholar
  39. Q. Zhu, Y. Zhong, B. Zhao, G.-S. Xia, and L. Zhang, “Bag-of-Visual-Words Scene Classifier with Local and Global Features for High Spatial Resolution Remote Sensing Imagery,” IEEE Geoscience and Remote Sensing Letters, vol. 13, no. 6, pp. 747–751, 2016. View at Publisher · View at Google Scholar · View at Scopus