Desktop version

Home arrow Engineering arrow Cellular Image Classification

Summary

In this chapter, we have investigated into the BoW and SPM framework for image classification. The target of BoW and SPM framework is to embed low-level descriptors in a representative codebook space. There are three main steps for generating the corresponding representation, which are feature extraction, coding and pooling. Each step within the framework can affect the quality of image representation and the following classification performance. We have introduced various coding and pooling methods.

References

  • 1. J. Yang, K. Yu, Y. Gong, and T. Huang. Linear spatial pyramid matching using sparse coding for image classification. In Proc. CVPR, pages 1794-1801, 2009.
  • 2. J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong. Locality-constrained linear coding for image classification. In Proc. CVPR, pages 3360-3367, 2010.
  • 3. Linlin Shen, Jiaming Lin, Shengyin Wu, and Shiqi Yu. Hep-2 image classification using intensity order pooling based features and bag of words. Pattern Recognition, 47(7):2419-2427, 2014.
  • 4. Arnold Wiliem, Conrad Sanderson, Yongkang Wong, Peter Hobson, Rodney F Minchin, and Brian C Lovell. Automatic classification of human epithelial type 2 cell indirect immunofluorescence images using cell pyramid matching. Pattern Recognition, 47(7):2315-2324, 2014.
  • 5. D.G. Lowe. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis., 60(2):91-110, 2004.
  • 6. Navneet Dalal and Bill Triggs. Histograms of oriented gradients for human detection. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, volume 1, pages 886-893. IEEE, 2005.
  • 7. Timo Ojala, Matti Pietikainen, and David Harwood. A comparative study of texture measures with classification based on featured distributions. Pattern recognition, 29(1):51-59, 1996.
  • 8. Jianchao Yang, Kai Yu, and Thomas Huang. Supervised translation-invariant sparse coding. In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pages 35173524. IEEE, 2010.
  • 9. Julien Mairal, Jean Ponce, Guillermo Sapiro, Andrew Zisserman, and Francis R Bach. Supervised dictionary learning. In Advances in neural information processing systems, pages 10331040, 2009.
  • 10. Zhuolin Jiang, Guangxiao Zhang, and Larry S Davis. Submodular dictionary learning for sparse coding. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pages 3418-3425. IEEE, 2012.
  • 11. K. Yu, T. Zhang, and Yihong Gong. Nonlinear learning using local coordinate coding. In Proc. NIPS, pages 2223-2231, 2009.
  • 12. Svetlana Lazebnik, Cordelia Schmid, and Jean Ponce. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, volume 2, pages 2169-2178. IEEE, 2006.
  • 13. S. McCann and D.G. Lowe. Local naive bayes nearest neighbor for image classification. In Proc. CVPR, pages 3650-3656, 2012.
  • 14. Jan C van Gemert, Jan-Mark Geusebroek, Cor J Veenman, and Arnold WM Smeulders. Kernel codebooks for scene categorization. In Proc. ECCV, pages 696-709. Springer, 2008.
  • 15. Jan C van Gemert, Cor J Veenman, Arnold WM Smeulders, and J.M. Geusebroek. Visual word ambiguity. IEEE Trans. Pattern Anal. Mach. Intell., 32(7):1271-1283, 2010.
  • 16. L. Liu, L. Wang, and X. Liu. In defense of soft-assignment coding. In Proc. ICCV, pages 2486-2493,2011.
  • 17. Yongzhen Huang, Kaiqi Huang, Yinan Yu, and Tieniu Tan. Salient coding for image classification. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pages 1753-1760. IEEE, 2011.
  • 18. Zifeng Wu, Yongzhen Huang, Liang Wang, and Tieniu Tan. Group encoding of local features in image classification. In Pattern Recognition (ICPR), 2012 21st International Conference on, pages 1505-1508. IEEE, 2012.
  • 19. Y-Lan Boureau, Jean Ponce, and Yann LeCun. A theoretical analysis of feature pooling in visual recognition. In Proceedings of the 27th International Conference on Machine Learning (ICML-10), pages 111-118, 2010.
  • 20. Y-Lan Boureau, Francis Bach, Yann LeCun, and Jean Ponce. Learning mid-level features for recognition. In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pages 2559-2566. IEEE, 2010.
 
Source
< Prev   CONTENTS   Source   Next >