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Image Classification Using Probability Higher-Order Local Auto-Correlations

  • Tetsu Matsukawa
  • Takio Kurita
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5996)

Abstract

In this paper, we propose a novel method for generic object recognition by using higher-order local auto-correlations on probability images. The proposed method is an extension of bag-of-features approach to posterior probability images. Standard bag-of-features is approximately thought as sum of posterior probabilities on probability images, and spatial co-occurrences of posterior probability are not utilized. Thus, its descriptive ability is limited. However, using local auto-correlations of probability images, the proposed method extracts richer information than the standard bag-of-features. Experimental results show the proposed method is enable to have higher classification performances than the standard bag-of-features.

Keywords

Recognition Rate Training Image Probability Image Image Patch Mask Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Agarwal, A., Triggs, B.: Multilevel Image Coding with Hyperfeatures. International Journal of Computer Vision 78, 15–27 (2008)CrossRefGoogle Scholar
  2. 2.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)CrossRefGoogle Scholar
  3. 3.
    Csurka, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: Visual Categorization with Bag of Keypoints. In: European conference on computer vision 2004 workshop on Statistical Learning in Computer Vision, pp. 59–74 (2004)Google Scholar
  4. 4.
    Vapnik, V.: Statistical Learning Theory. John Wiley & Sons, Chichester (1998)zbMATHGoogle Scholar
  5. 5.
    Kobayashi, T., Otsu, N.: Image Feature Extraction Using Gradient Local Auto-Correlations. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 346–358. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  6. 6.
    Otsu, N., Kurita, T.: A new scheme for practical flexible and intelligent vision systems. In: IAPR Workshop on Computer Vision (1988)Google Scholar
  7. 7.
    Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2169–2178 (2006)Google Scholar
  8. 8.
    Marszalek, M., Schmid, C.: Spatial Weighting for Bag-of-Features. In: IEEE Conference on Conputer Vision and Pattern Recognition, vol. 2, pp. 2118–2125 (2006)Google Scholar
  9. 9.
    Jurie, F., Triggs, B.: Creating efficient codebooks for visual recognition. In: IEEE International Conference on Computer Vision, vol. 1, pp. 604–610 (2005)Google Scholar
  10. 10.
    Nowak, E., Jurie, F., Triggs, B.: Sampling strategies for bag-of-features image classification. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 490–503. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  11. 11.
    van Gemert, J.C., Geusebroek, J.-M., Veenman, C.J., Smeulders, A.W.M.: Kernel Codebooks for Scene Categorization. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 696–709. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  12. 12.
    Shechtman, E., Irani, M.: Matching local self-similarities across images and videos. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 511–518 (2007)Google Scholar
  13. 13.
    Savarse, S., Winn, J., Criminisi, A.: Discriminative Object Class Models of Appearance and Shape by Correlatons. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2033–2040 (2006)Google Scholar
  14. 14.
    Bosch, A., Zisserman, A., Munoz, X.: Image classification using random forests and ferns. In: IEEE International Conference on Computer Vision, pp. 1–8 (2007)Google Scholar
  15. 15.
    Shotton, J., Johnson, M., Cipolla, R.: Semantic texton forests for image categorization and segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)Google Scholar
  16. 16.
    Wang, X., Grimson, E.: Spatial Latent Dirichlet Allocation. In: Proceedings of Neural Information Processing Systems Conference, NIPS (2007)Google Scholar
  17. 17.
    Quack, T., Ferrari, V., Leibe, B., Van Gool, L.: Efficient mining of frequent and distinctive feature configurations. In: IEEE International Conference on Computer Vision (2007)Google Scholar
  18. 18.
    Yuan, J., Wu, Y., Yang, M.: Discovery of Collocation Patterns: from Visual Words to Visual Phrases. In: IEEE Conference on Conputer Vision and Pattern Recognition (2007)Google Scholar
  19. 19.
    Zheng, Y.-T., Zhao, M., Neo, S.-Y., Chua, T.-S., Tian, Q.: Visual Synset: Towards a Higher-level Visual Representation. In: IEEE Conference on Computer Vision and Pattern Recognition (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Tetsu Matsukawa
    • 1
  • Takio Kurita
    • 2
  1. 1.University of TsukubaTsukubaJapan
  2. 2.National Institute of Advanced Industrial Science and TechnologyTsukubaJapan

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