Local binary patterns

Local binary patterns (LBP) is a type of feature used for classification in computer vision. LBP is the particular case of the Texture Spectrum model proposed in 1990.^[1][2] LBP was first described in 1994.^[3][4] It has since been found to be a powerful feature for texture classification; it has further been determined that when LBP is combined with the Histogram of oriented gradients (HOG) descriptor, it improves the detection performance considerably on some datasets.^[5]

Concept

 

Three neighborhood examples used to define a texture and calculate a local binary pattern (LBP)

The LBP feature vector, in its simplest form, is created in the following manner:

• Divide the examined window into cells (e.g. 16x16 pixels for each cell).
• For each pixel in a cell, compare the pixel to each of its 8 neighbors (on its left-top, left-middle, left-bottom, right-top, etc.). Follow the pixels along a circle, i.e. clockwise or counter-clockwise.
• Where the center pixel's value is greater than the neighbor's value, write "1". Otherwise, write "0". This gives an 8-digit binary number (which is usually converted to decimal for convenience).
• Compute the histogram, over the cell, of the frequency of each "number" occurring (i.e., each combination of which pixels are smaller and which are greater than the center).
• Optionally normalize the histogram.
• Concatenate (normalized) histograms of all cells. This gives the feature vector for the window.

The feature vector can now be processed using the Support vector machine or some other machine-learning algorithm to classify images. Such classifiers can be used for face recognition or texture analysis.

Implementations

• CMV, includes the general LBP implementation and many further extensions over LBP histogram in MATLAB.
• Python mahotas, an open source computer vision package which includes an implementation of LBPs.
•  OpenCV's Cascade Classifiers support LBPs as of version 2.
• VLFeat, a open source computer vision library in C (with bindings to multiple languages including MATLAB) has an implementation.
• bgslibrary includes the original LBP implementation for motion detection^[6] as well as a new LBP operator variant combined with Markov Random Fields^[7] with improved recognition rates and robustness.

See also

• Local ternary patterns
• Local Binary Pattern (LBP) methodology in Scholarpedia

References

1. DC. He and L. Wang (1990), "Texture Unit, Texture Spectrum, And Texture Analysis", Geoscience and Remote Sensing, IEEE Transactions on, vol. 28, pp. 509 - 512.
2. L. Wang and DC. He (1990), "Texture Classification Using Texture Spectrum", Pattern Recognition, Vol. 23, No. 8, pp. 905 - 910.
3. T. Ojala, M. Pietikäinen, and D. Harwood (1994), "Performance evaluation of texture measures with classification based on Kullback discrimination of distributions", Proceedings of the 12th IAPR International Conference on Pattern Recognition (ICPR 1994), vol. 1, pp. 582 - 585.
4. T. Ojala, M. Pietikäinen, and D. Harwood (1996), "A Comparative Study of Texture Measures with Classification Based on Feature Distributions", Pattern Recognition, vol. 29, pp. 51-59.
5. "An HOG-LBP Human Detector with Partial Occlusion Handling", Xiaoyu Wang, Tony X. Han, Shuicheng Yan, ICCV 2009
6. M. Heikkilä, M. Pietikäinen, "A texture-based method for modeling the background and detecting moving objects", IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(4):657-662, 2006.
7. C., Kertész: Texture-Based Foreground Detection, International Journal of Signal Processing, Image Processing and Pattern Recognition (IJSIP), Vol. 4, No. 4, 2011.