Advertisement

Pairwise Rotation Invariant Co-occurrence Local Binary Pattern

  • Xianbiao Qi
  • Rong Xiao
  • Jun Guo
  • Lei Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7577)

Abstract

In this work, we introduce a novel pairwise rotation invariant co-occurrence local binary pattern (PRI-CoLBP) feature which incorporates two types of context - spatial co-occurrence and orientation co-occurrence. Different from traditional rotation invariant local features, pairwise rotation invariant co-occurrence features preserve relative angle between the orientations of individual features. The relative angle depicts the local curvature information, which is discriminative and rotation invariant. Experimental results on the CUReT, Brodatz, KTH-TIPS texture dataset, Flickr Material dataset, and Oxford 102 Flower dataset further demonstrate the superior performance of the proposed feature on texture classification, material recognition and flower recognition tasks.

Keywords

Local Binary Pattern Local Binary Pattern Feature Local Binary Pattern Operator Material Recognition Local Binary Pattern Code 
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.
    Yuan, J., Yang, M., Wu, Y.: Mining discriminative co-occurrence patterns for visual recognition. In: CVPR (2011)Google Scholar
  2. 2.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. IJCV (2004)Google Scholar
  3. 3.
    Chang, P., Krumm, J.: Object recognition with color co-occurrence histograms. In: CVPR (1999)Google Scholar
  4. 4.
    Ito, S., Kubota, S.: Object Classification Using Heterogeneous Co-occurrence Features. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part II. LNCS, vol. 6312, pp. 209–222. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  5. 5.
    Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. PAMI (2002)Google Scholar
  6. 6.
    Haralick, R., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics (1973)Google Scholar
  7. 7.
    Nilsback, M., Zisserman, A.: Automated flower classification over a large number of classes. In: ICVGIP (2008)Google Scholar
  8. 8.
    Nilsback, M.: An automatic visual flora - segmentation and classification of flowers images. PhD thesis, University of Oxford (2009)Google Scholar
  9. 9.
    Chai, Y., Lempitsky, V., Zisserman, A.: Bicos: A bi-level co-segmentation method for image classification. In: ICCV (2011)Google Scholar
  10. 10.
    Brodatz, P.: Textures: a photographic album for artists and designers. Dover Publications, New York (1999)Google Scholar
  11. 11.
    Dana, K., Van Ginneken, B., Nayar, S., Koenderink, J.: Reflectance and texture of real-world surfaces. ACM Transactions on Graphics (TOG) (1999)Google Scholar
  12. 12.
    Hayman, E., Caputo, B., Fritz, M., Eklundh, J.-O.: On the Significance of Real-World Conditions for Material Classification. In: Pajdla, T., Matas, J. (eds.) ECCV 2004, Part IV. LNCS, vol. 3024, pp. 253–266. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  13. 13.
    Sharan, L., Rosenholtz, R., Adelson, E.: Material perception: What can you see in a brief glance? Journal of Vision (2009)Google Scholar
  14. 14.
    Zhang, J., Marszalek, M., Lazebnik, S., Schmid, C.: Local features and kernels for classification of texture and object categories: a comprehensive study. In: CVPR (2007)Google Scholar
  15. 15.
    Vedaldi, A., Zisserman, A.: Efficient additive kernels via explicit feature maps. In: CVPR (2010)Google Scholar
  16. 16.
    Varma, M., Zisserman, A.: A statistical approach to material classification using image patch exemplars. PAMI (2008)Google Scholar
  17. 17.
    Caputo, B., Hayman, E., Fritz, M., Eklundh, J.: Classifying materials in the real world. Image and Vision Computing (2010)Google Scholar
  18. 18.
    Nguyen, H., Fablet, R., Boucher, J.: Visual textures as realizations of multivariate log-gaussian cox processes. In: CVPR (2011)Google Scholar
  19. 19.
    Lazebnik, S., Schmid, C., Ponce, J.: A sparse texture representation using local affine regions. PAMI (2005)Google Scholar
  20. 20.
    Hu, D., Bo, L.: Toward robust material recognition for everyday objects. In: BMVC (2011)Google Scholar
  21. 21.
    Liu, C., Sharan, L., Adelson, E., Rosenholtz, R.: Exploring features in a bayesian framework for material recognition. In: CVPR (2010)Google Scholar
  22. 22.
    Rother, C., Kolmogorov, V., Blake, A.: Grabcut: Interactive foreground extraction using iterated graph cuts. ACM Transactions on Graphics (TOG) (2004)Google Scholar
  23. 23.
    Yuan, X., Yan, S.: Visual classification with multi-task joint sparse representation. In: CVPR (2010)Google Scholar
  24. 24.
    Kanan, C., Cottrell, G.: Robust classification of objects, faces, and flowers using natural image statistics. In: CVPR (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Xianbiao Qi
    • 1
  • Rong Xiao
    • 2
  • Jun Guo
    • 1
  • Lei Zhang
    • 2
  1. 1.Beijing University of Posts and TelecommunicationsBeijingP.R. China
  2. 2.Microsoft Research AsiaBeijingP.R. China

Personalised recommendations