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Contextual Information Guided Image Categorization Algorithm

  • Hong Shen
  • Tian_Gong Li
  • Zhen-heng Zhang
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 99)

Abstract

This paper proposes a method for scene categorization by integrating region contextual information into the popular Bag-of-Visual-Words approach. The Bag-of-Visual-Words approach describes an image as a bag of discrete visual words, where the frequency distributions of these words are used for image categorization. However, the traditional visual words suffer from the problem when faced these patches with similar appearances but distinct semantic concepts. This paper introduces an improved contextual CRF model to learn each visual word simultaneously depending on itself and the rest of the visual words in the same region. The experimental results on the three well-known datasets show that region contextual visual words indeed improves categorization performance compared to traditional visual words.

Keywords

Image categorization Conditional Random Fields Bag of visual words 

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References

  1. 1.
    Oliva, A., Torralba, A.: Modeling the shape of the scene: A holistic representation of the spatial envelope. International Journal of Computer Vision 42(3), 145–175 (2001)zbMATHCrossRefGoogle Scholar
  2. 2.
    Vogel, J., Schiele, B.: Semantic modeling of natural scenes for content-based image retrieval. International Journal of Computer Vision 72(2), 133–157 (2007)CrossRefGoogle Scholar
  3. 3.
    Boutell, M., Luo, J., Brown, C.: Factor-graphs for region-based whole-scene classification. In: Proc. of IEEE Int. on Computer Vision and Pattern Recognition Workshop (CVPRW 2006), USA, p. 104 (2006)Google Scholar
  4. 4.
    Florent, M., Pedro, Q.: Integrating co-occurrence and spatial contexts on patch-based scene segmentation. In: Proc. of IEEE Int. on Computer Vision and Pattern Recognition Workshop (CVPRW 2006), USA, p. 14 (2006)Google Scholar
  5. 5.
    Bosch, A., Zisserman, A., Munoz, X.: Image classification using random forests and ferns. In: Proc.of IEEE Int. Conf. on Computer Vsion (ICCV 2007), Brazil, pp. 1–8 (2007)Google Scholar
  6. 6.
    Bosch, A., Zisserman, A.: Scene classification using a hybrid generative/discriminative approach. IEEE Trans. on Pattern Analysis and Machine Intelligence 30(4), 712–727 (2008)CrossRefGoogle Scholar
  7. 7.
    Jingen, L., Mubarak, S.: Scene Modeling Using Co-Clustering. In: Proc. of IEEE Int. Conf. on Computer Vsion (ICCV 2007), Brazil, pp. 1–7 (2007)Google Scholar
  8. 8.
    Winn, J., Criminisi, A., Minka, T.: Object categorization by learned universal visual dictionary. In: Proc. of IEEE Int. Conf. on Computer Vsion (ICCV 2005), China, pp. 1800–1807 (2005)Google Scholar
  9. 9.
    Perronnin, F., Dance, C., Csurka, G., Bressan, M.: Adapted vocabularies for generic visual categorization. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 464–475. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  10. 10.
    Jurie, F., Triggs, B.: Creating efficient codebooks for visual recognition. In: Proc. of IEEE Int. Conf. on Computer Vsion (ICCV 2005), China, pp. 604–610 (2005)Google Scholar
  11. 11.
    Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: Proc. of IEEE Int. Conf. on Computer Vision and Pattern Recognition (CVPR 2006), USA, pp. 2169–2178 (2006)Google Scholar
  12. 12.
    Lafferty, J., McCallum, A., Pereira, F.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proc. of Int. Conf. on Machine Learning (ICML 2001), USA, pp. 282–289 (2001)Google Scholar
  13. 13.
    Lowe, D.: Distinctive image features from scale-invariant key points. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  14. 14.
    Deng, Y., Manjunath, B.: Unsupervised segmentation of color-texture regions in images and video. IEEE Trans. on Pattern Analysis and Machine Intelligence 23(8), 800–810 (2001)CrossRefGoogle Scholar
  15. 15.
    Jan, C., Gemert, V.: Kernelk 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
  16. 16.
    Fei-Fei, L., Perona, P.: A Bayesian hierarchical model for learning natural scene categories. In: Proc. of IEEE Int. Conf. on Computer Vision and Pattern Recognition (CVPR 2005), USA, pp. 524–531 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Hong Shen
    • 1
  • Tian_Gong Li
    • 1
  • Zhen-heng Zhang
    • 1
  1. 1.Institute of Information TechnologyBeijing Union UniversityBeijingChina

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