Local binary pattern (LBP), a very important feature in the field of machine vision. LBP can effectively deal with illumination changes and is widely used in texture analysis and texture recognition.
The LBP algorithm is very simple. Simply put, the gray value of a pixel in the image is compared with the gray value of the pixel in its neighborhood, as shown in the following figure:
If the neighborhood pixel value is larger than the point, it is assigned to 1, and vice versa, it is assigned to 0. Thus, starting from the upper left corner, a bit chain can be formed, and then the bit chain is converted into a decimal number. Expressed as follows:
Where s represents a threshold function that satisfies the following relationship:
Through this conversion, the difference relationship between a pixel and the neighborhood can be represented by a number, because the LBP records the difference relationship between the pixel and the neighboring pixel, so the illumination change causes the same increase and decrease of the pixel value. Will change the size of the LBP, especially in the local area, we can think that the pixel value change caused by the illumination is unidirectional, so LBP can well preserve the difference value of the pixel values ​​in the image. The LBP can be further used for histogram statistics, which can be used as a feature operator for texture analysis.
We can see that R represents the radius of the neighborhood, P represents the number of neighboring pixels, or the length of the bit chain. If the radius of the neighborhood is 1, the number of pixels in the neighborhood is 8, and the length of the bit chain is 8. If the neighborhood radius is 2, the number of pixels in the neighborhood is 16, the length of the bit chain is 16, the radius of the neighborhood is 3, the number of pixels in the neighborhood is 24, and the length of the bit chain is 24, as shown in the following figure. Show:
Let us consider the simplest case of R=1, P=8. The LBP maps to a value between 0 and 255. If represented by a histogram, a 256-dimensional array is needed to store the histogram. In order to reduce the storage space, someone has proposed the encoding method of uniform pattern, which defines the uniform pattern according to the number of conversions between 0 and 1 in a bit chain. If there is no more than two conversions between 0 and 1 in a bit chain, then the bit chain is a uniform pattern. For example, 00000000 conversion times is 0, 00001111 conversion times is 1, 00011100 conversion times is 2, 01101100 conversion times For 4, 01101010, the number of conversions is 6, and those whose conversion times are not more than twice are all uniform patterns. It can be proved that most of the binary patterns are uniform patterns. With this definition, for 8-bit LBP, you can The 256-dimensional drop to 59-dimensional, a reduction of 90%.
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