Constructing Local Binary Pattern Statistics by Soft Voting
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.
- 2.Pietikäinen, M., Hadid, A., Zhao, G., Ahonen, T.: Computer Vision Using Local Binary Patterns. Springer (2011)Google Scholar
- 4.Ahonen, T., Pietikäinen, M.: Soft histograms for local binary patterns. In: Proc. Finnish Signal Processing Symposium (2007)Google Scholar
- 6.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)Google Scholar
- 8.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)Google Scholar
- 9.Guo, Z., Zhang, L., Zhang, D.: A completed modeling of local binary pattern operator for texture classification. IEEE TIP 19(6), 1657–1663 (2010)Google Scholar
- 10.Bishop, C.M.: Pattern recognition and machine learning. Springer (2006)Google Scholar