Abstract
In this paper we propose a novel method for constructing Local Binary Pattern (LBP) statistics for image appearance description. The method is inspired by the kernel density estimation designed for estimating the underlying probability function of a random variable. An essential part of the proposed method is the use of Hamming distance. Compared to the standard LBP histogram statistics where one labeled pixel always contributes to one bin of the histogram, the proposed method exploits a kernel-like similarity function to determine weighted votes contributing several possible pattern types in the statistic. As a result, the method yields a more reliable estimate of the underlying LBP distribution of the given image. In overall, the method is easy to implement and outperforms the standard LBP histogram description in texture classification and in biometrics-related face verification. We demonstrate that the method is extremely potential in problems where the number of pixels is limited. This makes the method very promising, for example, in low-resolution image description and the description of interest regions. Another interesting property of the proposed method is that it can be easily integrated with many existing LBP variants that use label statistics as descriptors.
Chapter PDF
Similar content being viewed by others
References
Wolf, L., Hassner, T., Taigman, Y.: Effective unconstrained face recognition by combining multiple descriptors and learned background statistics. IEEE TPAMI 33(10), 1978–1990 (2011)
Pietikäinen, M., Hadid, A., Zhao, G., Ahonen, T.: Computer Vision Using Local Binary Patterns. Springer (2011)
Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE TPAMI 24(7), 971–987 (2002)
Ahonen, T., Pietikäinen, M.: Soft histograms for local binary patterns. In: Proc. Finnish Signal Processing Symposium (2007)
Ahonen, T., Pietikäinen, M.: Pixelwise local binary pattern models of faces using kernel density estimation. In: Tistarelli, M., Nixon, M.S. (eds.) ICB 2009. LNCS, vol. 5558, pp. 52–61. Springer, Heidelberg (2009)
Yang, H., Wang, Y.: A LBP-based face recognition method with hamming distance constraint. Image and Graphics. In: Fourth International Conference on Image and Graphics, ICIG 2007, pp. 645–649 (2007)
Ylioinas, J., Hadid, A., Guo, Y., Pietikäinen, M.: Efficient image appearance description using dense sampling based local binary patterns. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012, Part III. LNCS, vol. 7726, pp. 375–388. Springer, Heidelberg (2013)
Huang, G., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled Faces in the Wild: A database for studying face recognition in unconstrained environments. UMass Amherst Technical Report 07-49 (October 2007)
Guo, Z., Zhang, L., Zhang, D.: A completed modeling of local binary pattern operator for texture classification. IEEE TIP 19(6), 1657–1663 (2010)
Bishop, C.M.: Pattern recognition and machine learning. Springer (2006)
Aitchison, J., Aitken, C.: Multivariate binary discrimination by the kernel method. Biometrika 63(3), 413–420 (1976)
Dana, K.J., van Ginneken, B., Nayar, S.K., Koenderink, J.J.: Reflectance and texture of real-world surfaces. ACM Transactions on Graphics 18, 1–34 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ylioinas, J., Hong, X., Pietikäinen, M. (2013). Constructing Local Binary Pattern Statistics by Soft Voting. In: Kämäräinen, JK., Koskela, M. (eds) Image Analysis. SCIA 2013. Lecture Notes in Computer Science, vol 7944. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38886-6_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-38886-6_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38885-9
Online ISBN: 978-3-642-38886-6
eBook Packages: Computer ScienceComputer Science (R0)