Local binary patterns

Local binary patterns (LBP) is a type of visual descriptor 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:

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.

A useful extension to the original operator is the so-called uniform pattern[8], which can be used to reduce the length of the feature vector and implement a simple rotation invariant descriptor. This idea is motivated by the fact that some binary patterns occur more commonly in texture images than others. A local binary pattern is called uniform if the binary pattern contains at most two 0-1 or 1-0 transitions. For example, 00010000(2 transitions) is a uniform pattern, 01010100(6 transitions) is not. In the computation of the LBP histogram, the histogram has a separate bin for every uniform pattern, and all non-uniform patterns are assigned to a single bin. Using uniform patterns, the length of the feature vector for a single cell reduces from 256 to 59.

Extensions

Implementations

See also

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. Trefný, Jirí, and Jirí Matas."Extended set of local binary patterns for rapid object detection." Proceedings of the Computer Vision Winter Workshop. Vol. 2010. 2010.
  7. Zhao, Guoying, and Matti Pietikainen. "Dynamic texture recognition using local binary patterns with an application to facial expressions." Pattern Analysis and Machine Intelligence, IEEE Transactions on 29.6 (2007): 915-928.
  8. 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.
  9. C., Kertész: Texture-Based Foreground Detection, International Journal of Signal Processing, Image Processing and Pattern Recognition (IJSIP), Vol. 4, No. 4, 2011.
This article is issued from Wikipedia - version of the Saturday, February 20, 2016. The text is available under the Creative Commons Attribution/Share Alike but additional terms may apply for the media files.