Advertisement

On Vocabulary Size in Bag-of-Visual-Words Representation

  • Jian Hou
  • Jianxin Kang
  • Naiming Qi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6297)

Abstract

Bag-of-visual-words is a popular image representation that produces high matching accuracy and efficiency. While vocabulary size impacts on matching accuracy, existing research usually selects the vocabulary size empirically. Research on representative local descriptors shows that with similarity based clustering, the intra-cluster similarity extent of descriptors plays the same role in straightforward matching as vocabulary size in visual words matching. Based on this observation, we propose to use similarity based clustering to determine the optimal vocabulary size for a given dataset in visual words matching. Preliminary experiments with three datasets produce encouraging results and demonstrate the potential of the proposed approach.

Keywords

Visual words vocabulary size straightforward matching  representative local descriptors 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar
  2. 2.
    Deselaers, T., Keysers, D., Ney, H.: Features for Image Retrieval: an Experimental Comparison. Inf. Retr. 11(2), 77–107 (2008)CrossRefGoogle Scholar
  3. 3.
    Mikolajczyk, K., Schmid, C.: A Performance Evaluation of Local Descriptors. IEEE Trans. Pattern Anal. Machine Intell. 27(10), 1615–1630 (2005)CrossRefGoogle Scholar
  4. 4.
    Ke, Y., Sukthankar, R.: PCA-SIFT: a More Distinctive Representation for Local Image Descriptors. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 511–517. IEEE Press, New York (2004)Google Scholar
  5. 5.
    Brown, M., Szeliski, R., Winder, S.: Multi-Image Matching Using Multi-Scale Oriented Patches. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 510–517. IEEE Press, New York (2005)Google Scholar
  6. 6.
    Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust Wide-Baseline Stereo from Maximally Stable Extremal Regions. In: 13th British Machine Vision Conference, vol. 1, pp. 384–393. British Machine Vision Association, London (2002)Google Scholar
  7. 7.
    Tuytelaars, T., Gool, L.V.: Wide Baseline Stereo Matching Based on Local, Affinely Invariant Regions. In: 11th British Machine Vision Conference, pp. 412–425. British Machine Vision Association, London (2000)Google Scholar
  8. 8.
    Kadir, T., Zisserman, A., Brady, M.: An Affine Invariant Salient Region Detector. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 228–241. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  9. 9.
    Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Gool, L.V.: A Comparison of Affine Region Detectors. Int. J. Comput. Vis. 65(1-2), 43–72 (2006)CrossRefGoogle Scholar
  10. 10.
    Belongie, S., Malik, J., Puzicha, J.: Shape Matching and Object Recognition Using Shape Contexts. IEEE Trans. Pattern Anal. Machine Intell. 24(4), 509–522 (2002)CrossRefGoogle Scholar
  11. 11.
    Gool, L.V., Moons, T., Ungureanu, D.: Affine/Photometric Invariants for Planar Intensity Patterns. In: Buxton, B.F., Cipolla, R. (eds.) ECCV 1996. LNCS, vol. 1064, pp. 228–241. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  12. 12.
    Freeman, W.T., Adelson, E.H.: The Design and Use of Steerable Filters. IEEE Trans. Pattern Anal. Machine Intell. 13(9), 891–906 (1991)CrossRefGoogle Scholar
  13. 13.
    Lazebnik, S., Schmid, C., Ponce, J.: Sparse Texture Representation Using Affine-Invariant Neighborhoods. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 319–324. IEEE Press, New York (2003)Google Scholar
  14. 14.
    Zhang, W., Kosecka, J.: Hierarchical Building Recognition. Image Vis. Comput. 26(5), 704–716 (2007)CrossRefGoogle Scholar
  15. 15.
    Sivic, J., Zisserman, A.: Video Google: a Text Retrieval Approach to Object Matching in Videos. In: 9th IEEE International Conference on Computer Vision, pp. 1470–1477. IEEE Press, New York (2003)CrossRefGoogle Scholar
  16. 16.
    Deselaers, T., Keysers, D., Ney, H.: Discriminative Training for Object Recognition Using Image Patches. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 157–162. IEEE Press, New York (2005)Google Scholar
  17. 17.
    Mikolajczyk, K., Leibe, B., Schiele, B.: Multiple Object Class Detection with a Generative Model. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 26–36. IEEE Press, New York (2006)Google Scholar
  18. 18.
    Yang, J., Jiang, Y., Hauptmann, A., Ngo, C.W.: Evaluating Bag-of-Visual-Words Representations in Scene Classification. In: 9th ACM SIGMM International workshop on Multimedia Information Retrieval, pp. 197–206. ACM Press, New York (2007)CrossRefGoogle Scholar
  19. 19.
    Li, T., Mei, T., Kweon, I.S.: Learning Optimal Compact Codebook for Efficient Object Categorization. In: IEEE 2008 Workshop on Applications of Computer Vision, pp. 1–6. IEEE Press, New York (2008)Google Scholar
  20. 20.
    Deselaers, T., Pimenidis, L., Ney, H.: Bag-of-Visual-Words Models for Adult Image Classification and Filtering. In: International Conference on Pattern Recognition, pp. 1–4. IAPR, Tampa (2008)Google Scholar
  21. 21.
    Grauman, K., Darrell, T.: The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features. In: 10th IEEE International Conference on Computer Vision, vol. 2, pp. 1458–1465. IEEE Press, New York (2005)Google Scholar
  22. 22.
    Lazebnik, S., Schmid, C., Ponce, J.: Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 2169–2178. IEEE Press, New York (2006)Google Scholar
  23. 23.
    Nister, D., Stewenius, H.: Scalable Recognition with a Vocabulary Tree. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 2161–2168. IEEE Press, New York (2006)Google Scholar
  24. 24.
    Dorko, G., Schmid, C.: Selection of Scale-Invariant Parts for Object Class Recognition. In: 9th IEEE International Conference on Computer Vision, vol. 1, pp. 634–639. IEEE Press, New York (2003)CrossRefGoogle Scholar
  25. 25.
    Shao, H., Svoboda, T., Gool, L.V.: ZUBUD-Zurich Building Database for Image Based Recognition. Technical report No. 260, Swiss Federal Institute of Technology (2003)Google Scholar
  26. 26.
    Zhang, J., Marszalek, M., Lazebnik, S., Schmid, C.: Local Features and Kernels for Classification of Texture and Object Categories: An in-depth Study. Technical report, INRIA (2003)Google Scholar
  27. 27.
    Zhao, W., Jiang, Y., Ngo, C.: Keyframe retrieval by keypoints: Can point-to-point Matching Help? In: ACM International Conference on Image and Video Retrieval, pp. 72–81. ACM Press, New York (2006)Google Scholar
  28. 28.
    Hou, J., Qi, N., Kang, J.: Image Matching Based on Representative Local Descriptors. In: Boll, S., Tian, Q., Zhang, L., Zhang, Z., Chen, Y.-P.P. (eds.) MMM 2010. LNCS, vol. 5916, pp. 303–313. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  29. 29.
    Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. In: CVPR, Workshop on Generative-Model Based Vision. IEEE Press, New York (2004)Google Scholar
  30. 30.
    Chua, T.-S., Tang, J., Hong, R., Li, H., Luo, Z., Zheng, Y.: NUS-WIDE: A Real-World Web Image Database from National University of Singapore. In: ACM International Conference on Image and Video Retrieval, pp. 1–9. ACM Press, New York (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Jian Hou
    • 1
  • Jianxin Kang
    • 1
    • 2
  • Naiming Qi
    • 1
  1. 1.School of AstronauticsHarbin Institute of TechnologyHarbinChina
  2. 2.School of EngineeringNortheast Agriculture UniversityHarbinChina

Personalised recommendations