Advertisement

Analysis of Sampling Techniques for Learning Binarized Statistical Image Features Using Fixations and Salience

  • Hamed Rezazadegan Tavakoli
  • Esa Rahtu
  • Janne Heikkilä
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8926)

Abstract

This paper studies the role of different sampling techniques in the process of learning Binarized Statistical Image Features (BSIF). It considers various sampling approaches including random sampling and selective sampling. The selective sampling utilizes either human eye tracking data or artificially generated fixations. To generate artificial fixations, this paper exploits salience models which apply to key point localization. Therefore, it proposes a framework grounded on the hypothesis that the most salient point conveys important information. Furthermore, it investigates possible performance gain by training BSIF filters on class specific data. To summarize, the contribution of this paper are as follows: 1) it studies different sampling strategies to learn BSIF filters, 2) it employs human fixations in the design of a binary operator, 3) it proposes an attention model to replicate human fixations, and 4) it studies the performance of learning application specific BSIF filters using attention modeling.

Keywords

Binary operators Visual attention Salience modeling 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bay, H., Ess, A., Tuytelaars, T., Gool, L.V.: Speeded-up robust features (surf). Computer Vision and Image Understanding 110(3), 346–359 (2008). Similarity Matching in Computer Vision and MultimediaCrossRefGoogle Scholar
  2. 2.
    Calonder, M., Lepetit, V., Ozuysal, M., Trzcinski, T., Strecha, C., Fua, P.: BRIEF: Computing a Local Binary Descriptor Very Fast. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(7), 1281–1298 (2012)CrossRefGoogle Scholar
  3. 3.
    Dana, K., Ginneken, B., Nayar, S., Koenderink, J.: Reflectance and texture of real world surfaces. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 151–157 (1997)Google Scholar
  4. 4.
    He, C., Ahonen, T., Pietikainen, M.: A bayesian local binary pattern texture descriptor. In: 19th International Conference on Pattern Recognition, ICPR 2008, pp. 1–4, December 2008Google Scholar
  5. 5.
    Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Tech. Rep. 07–49. University of Massachusetts, Amherst, October 2007Google Scholar
  6. 6.
    Hyvärinen, A., Hurri, J., Hoyer, P.O.: Natural Image Statistics A probabilistic approach to early computational vision. Springer (2009)Google Scholar
  7. 7.
    Judd, T., Ehinger, K., Durand, F., Torralba, A.: Learning to predict where humans look. In: IEEE International Conference on Computer Vision (ICCV) (2009)Google Scholar
  8. 8.
    Kanan, C., Cottrell, G.: Robust classification of objects, faces, and flowers using natural image statistics. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2472–2479, June 2010Google Scholar
  9. 9.
    Kannala, J., Rahtu, E.: Bsif: binarized statistical image features. In: ICPR (2012)Google Scholar
  10. 10.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  11. 11.
    Ojala, T., Mäenpää, T., Pietikäinen, M., Viertola, J., Kyllönen, J., Huovinen, S.: Outex - new framework for empirical evaluation of texture analysis algorithms. In: 16th International Conference on Pattern Recognition (2002)Google Scholar
  12. 12.
    Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)CrossRefGoogle Scholar
  13. 13.
    Ojansivu, V., Heikkilä, J.: Blur insensitive texture classification using local phase quantization. In: Elmoataz, A., Lezoray, O., Nouboud, F., Mammass, D. (eds.) ICISP 2008. LNCS, vol. 5099, pp. 236–243. Springer, Heidelberg (2008) CrossRefGoogle Scholar
  14. 14.
    Phillips, P., Wechsler, H., Huang, J., Rauss, P.J.: The feret database and evaluation procedure for face-recognition algorithms. Image and Vision Computing 16(5), 295–306 (1998)CrossRefGoogle Scholar
  15. 15.
    Shan, H., Cottrell, G.: Looking around the backyard helps to recognize faces and digits. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8 (2008)Google Scholar
  16. 16.
    Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: Deepface: Closing the gap to human-level performance in face verification. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2014)Google Scholar
  17. 17.
    Tistarelli, M., Nixon, M.S. (eds.): ICB 2009. LNCS, vol. 5558. Springer, Heidelberg (2009) Google Scholar
  18. 18.
    Tola, E., Lepetit, V., Fua, P.: DAISY: An Efficient Dense Descriptor Applied to Wide Baseline Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(5), 815–830 (2010)CrossRefGoogle Scholar
  19. 19.
    Varma, M., Zisserman, A.: A statistical approach to texture classification from single images. Int. J. Comput. Vision 62(1–2), 61–81 (2005)CrossRefGoogle Scholar
  20. 20.
    Ylioinas, J., Kannala, J., Hadid, A., Pietikäinen, M.: Learning local image descriptors using binary decision trees. In: WACV (2014)Google Scholar
  21. 21.
    Zhang, L., Tong, M.H., Marks, T.K., Shan, H., Cottrell, G.W.: Sun: A bayesian framework for saliency using natural statistics. Journal of Vision 8(7) (2008)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Hamed Rezazadegan Tavakoli
    • 1
  • Esa Rahtu
    • 1
  • Janne Heikkilä
    • 1
  1. 1.Center for Machine Vision ResearchUniversity of OuluOuluFinland

Personalised recommendations