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Visual Recognition Using Local Quantized Patterns

  • Sibt ul Hussain
  • Bill Triggs
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7573)

Abstract

Features such as Local Binary Patterns (LBP) and Local Ternary Patterns (LTP) have been very successful in a number of areas including texture analysis, face recognition and object detection. They are based on the idea that small patterns of qualitative local gray-level differences contain a great deal of information about higher-level image content. Current local pattern features use hand-specified codings that are limited to small spatial supports and coarse graylevel comparisons. We introduce Local Quantized Patterns (LQP), a generalization that uses lookup-table-based vector quantization to code larger or deeper patterns. LQP inherits some of the flexibility and power of visual word representations without sacrificing the run-time speed and simplicity of local pattern ones. We show that it outperforms well-established features including HOG, LBP and LTP and their combinations on a range of challenging object detection and texture classification problems.

Keywords

Face Recognition Object Detection Local Binary Pattern Local Pattern Average Precision 
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.

References

  1. 1.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE TPAMI (2002)Google Scholar
  2. 2.
    Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: Application to face recognition. IEEE TPAMI 28 (2006)Google Scholar
  3. 3.
    Pietikäinen, M., Hadid, A., Zhao, G., Ahonen, T.: Computer Vision Using Local Binary Patterns. Springer (2011)Google Scholar
  4. 4.
    Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE TIP 19, 1635–1650 (2010)MathSciNetGoogle Scholar
  5. 5.
    Chen, J., Shan, S., He, C., Zhao, G., Pietikainen, M., Chen, X., Gao, W.: WLD: A robust local image descriptor. IEEE TPAMI 32, 1705–1720 (2010)CrossRefGoogle Scholar
  6. 6.
    Hussain, S., Triggs, B.: Feature sets and dimensionality reduction for visual object detection. In: BMVC, pp. 112.1–112.10 (2010)Google Scholar
  7. 7.
    Leung, T., Malik, J.: Recognizing surfaces using three-dimensional textons. In: ICCV, pp. 1010–1017 (1999)Google Scholar
  8. 8.
    Csurka, G., Bray, C., Dance, C., Fan, L.: Visual categorization with bags of keypoints. In: Workshop on Statistical Learning in Computer Vision, ECCV (2004)Google Scholar
  9. 9.
    Varma, M., Zisserman, A.: Classifying Images of Materials: Achieving Viewpoint and Illumination Independence. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part III. LNCS, vol. 2352, pp. 255–271. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  10. 10.
    Tuytelaars, T., Schmid, C.: Vector quantizing feature space with a regular lattice. In: ICCV, pp. 1–8. IEEE (2007)Google Scholar
  11. 11.
    Cao, Z., Yin, Q., Tang, X., Sun, J.: Face recognition with learning-based descriptor. In: CVPR, pp. 2707–2714. IEEE (2010)Google Scholar
  12. 12.
    Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: Binary Robust Independent Elementary Features. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 778–792. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  13. 13.
    Elkan, C.: Using the triangle inequality to accelerate K-Means. In: ICML, vol. 20, pp. 147–153 (2003)Google Scholar
  14. 14.
    Moosmann, F., Triggs, B., Jurie, F.: Fast discriminative visual codebooks using randomized clustering forests. In: NIPS, vol. 19, p. 985 (2007)Google Scholar
  15. 15.
    Moosmann, F., Nowak, E., Jurie, F.: Randomized clustering forests for image classification. IEEE TPAMI, 1632–1646 (2008)Google Scholar
  16. 16.
    Brodatz, P.: Textures: a photographic album for artists and designers, vol. 66. Dover, New York (1966)Google Scholar
  17. 17.
    Valkealahti, K., Oja, E.: Reduced multidimensional co-occurrence histograms in texture classification. IEEE TPAMI 20, 90–94 (1998)CrossRefGoogle Scholar
  18. 18.
    Dana, K., Van Ginneken, B., Nayar, S., Koenderink, J.: Reflectance and texture of real-world surfaces. ACM Transactions on Graphics (TOG) 18, 1–34 (1999)CrossRefGoogle Scholar
  19. 19.
    Caputo, B., Hayman, E., Mallikarjuna, P.: Class-specific material categorisation. In: ICCV, pp. 1597–1604. IEEE (2005)Google Scholar
  20. 20.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR, pp. 886–893 (2005)Google Scholar
  21. 21.
    Everingham, M., Van Gool, L., Williams, C., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge. IJCV (2010)Google Scholar
  22. 22.
    Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part based models. TPAMI (Release 4)Google Scholar
  23. 23.
    Joachims, T.: Making large-scale SVM learning practical. In: Schölkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods - Support Vector Learning. The MIT Press, Cambridge (1999)Google Scholar
  24. 24.
    Papageorgiou, C., Poggio, T.: A trainable system for object detection. IJCV 38, 15–33 (2000)zbMATHCrossRefGoogle Scholar
  25. 25.
    Viola, P., Jones, M.J.: Robust real-time face detection. IJCV 57, 137–154 (2004)CrossRefGoogle Scholar
  26. 26.
    Hussain, S., Napoléon, T., Jurie, F.: Face recognition using local quantized patterns. In: BMVC (2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Sibt ul Hussain
    • 1
  • Bill Triggs
    • 2
  1. 1.GREYC, CNRS UMR 6072, Université de CaenFrance
  2. 2.Laboratoire Jean KuntzmannGrenobleFrance

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