Encoding Spatial Arrangement of Visual Words

  • Otávio A. B. Penatti
  • Eduardo Valle
  • Ricardo da S. Torres
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7042)

Abstract

This paper presents a new approach to encode spatial-relationship information of visual words in the well-known visual dictionary model. The current most popular approach to describe images based on visual words is by means of bags-of-words which do not encode any spatial information. We propose a graceful way to capture spatial-relationship information of visual words that encodes the spatial arrangement of every visual word in an image. Our experiments show the importance of the spatial information of visual words for image classification and show the gain in classification accuracy when using the new method. The proposed approach creates opportunities for further improvements in image description under the visual dictionary model.

Keywords

spatial-relationship visual words visual dictionaries 

References

  1. 1.
    Boureau, Y.L., Bach, F., LeCun, Y., Ponce, J.: Learning mid-level features for recognition. In: CVPR, pp. 2559–2566 (2010)Google Scholar
  2. 2.
    Cao, Y., Wang, C., Li, Z., Zhang, L., Zhang, L.: Spatial-bag-of-features. In: CVPR, pp. 3352–3359 (2010)Google Scholar
  3. 3.
    van Gemert, J.C., Veenman, C.J., Smeulders, A.W.M., Geusebroek, J.M.: Visual word ambiguity. TPAMI 32(7), 1271–1283 (2010)CrossRefGoogle Scholar
  4. 4.
    Griffin, G., Holub, A., Perona, P.: Caltech-256 object category dataset. Tech. Rep. 7694, California Institute of Technology (2007)Google Scholar
  5. 5.
    Hoíng, N.V., Gouet-Brunet, V., Rukoz, M., Manouvrier, M.: Embedding spatial information into image content description for scene retrieval. Pattern Recognition 43(9), 3013–3024 (2010)CrossRefMATHGoogle Scholar
  6. 6.
    Wenjun, L., Min, W.: Multimedia forensic hash based on visual words. In: ICIP, pp. 989–992 (2010)Google Scholar
  7. 7.
    Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: CVPR, vol. 2, pp. 2169–2178 (2006)Google Scholar
  8. 8.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. Journal of Comp. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar
  9. 9.
    Mikolajczyk, K., Schmid, C.: Scale and affine invariant interest point detectors. Int. Journal of Comp. Vis. 60, 63–86 (2004)CrossRefGoogle Scholar
  10. 10.
    Penatti, O.A.B., Torres, R.da.S.: Spatial relationship descriptor based on partitions. REIC 7(3) (2007) (in Portuguese)Google Scholar
  11. 11.
    Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: Lost in quantization: Improving particular object retrieval in large scale image databases. In: CVPR (2008)Google Scholar
  12. 12.
    Jianzhao, Q., Yung, N.: Category-specific incremental visual codebook training for scene categorization. In: ICIP, pp. 1501–1504 (2010)Google Scholar
  13. 13.
    Savarese, S., Winn, J., Criminisi, A.: Discriminative object class models of appearance and shape by correlatons. In: CVPR, vol. 2, pp. 2033–2040 (2006)Google Scholar
  14. 14.
    Sivic, J., Russell, B.C., Efros, A.A., Zisserman, A., Freeman, W.T.: Discovering objects and their location in images. In: ICCV, vol. 1, pp. 370–377 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Otávio A. B. Penatti
    • 1
  • Eduardo Valle
    • 1
  • Ricardo da S. Torres
    • 1
  1. 1.Recod Lab, Institute of ComputingUniversity of Campinas (Unicamp)CampinasBrazil

Personalised recommendations