Local Binary Patterns

Local Binary Pattern (LBP) is a simple yet very efficient texture operator which labels the pixels of an image by thresholding the neighborhood of each pixel and considers the result as a binary number. Due to its discriminative power and computational simplicity, LBP texture operator has become a popular approach in various applications. It can be seen as a unifying approach to the traditionally divergent statistical and structural models of texture analysis. Perhaps the most important property of the LBP operator in real-world applications is its robustness to monotonic gray-scale changes caused, for example, by illumination variations. Another important property is its computational simplicity, which makes it possible to analyze images in challenging real-time settings. LBP has been most widely used as a dense texture descriptor, but can also be used as a sparse descriptor like SIFT or computed on a grid like HOG.

For a more detailed description of LBP in spatial and spatiotemporal domains, see  LBP in Scholarpedia

Selected References

Ojala T, Pietikäinen M & Harwood D (1996) A comparative study of texture measures with classification based on feature distributions.  Pattern Recognition 29(1):51-59. 

Ojala T, Pietikäinen M & Mäenpää T (2002)  Multiresolution  gray-scale and rotation invariant texture classification with Local Binary Patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7):971-987. 

Heikkilä M & Pietikäinen M (2006) A texture-based method for modeling the background and detecting moving objects.  IEEE Transactions on  Pattern Analysis and Machine Intelligence 28(4):657-662. 

Zhao G & Pietikäinen M (2007) Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(6):915-928.

Heikkilä M, Pietikäinen M & Schmid C (2009) Description of interest regions with local binary patterns. Pattern Recognition 42(3):425-436.

Pietikäinen M, Hadid A, Zhao G & Ahonen T (2011) Computer Vision Using Local Binary Patterns. Springer, 207 p.

Guo Y, Zhao G & Pietikäinen M (2012) Discriminative features for texture description. Pattern Recognition 45(10):3834-3843.

Zhao G, Ahonen T, Matas J & Pietikäinen M (2012) Rotation-invariant image and video description with local binary pattern features. IEEE Transactions on Image Processing 21(4):1465-1467.

Chen J, Kellokumpu V, Zhao G & Pietikäinen M (2013) RLBP: Robust local binary pattern. Proc. the British Machine Vision Conference (BMVC 2013), Bristol, UK, 10 p.

Hong X, Zhao G, Pietikäinen M & Chen X (2014) Combining LBP difference and feature correlation for texture description. IEEE Transactions on Image Processing  23(6):2557 - 2568.

Pietikäinen M & Zhao G (2015) Two decades of local binary patterns: A survey. In: E Bingham, S Kaski, J Laaksonen & J Lampinen (eds) Advances in Independent Component Analysis and Learning Machines, Elsevier, 175-210.

Liu L, Lao S, Fieguth P, Guo Y, Wang X & Pietikäinen M (2016)  Median robust extended local binary pattern for texture classification. IEEE Transactions on Image Processing 25(3):1368-1381.

Last updated: 22.8.2016