Shulou(Shulou.com)06/02 Report--

Today, I will talk to you about the principle of LBP, which may not be well understood by many people. in order to make you understand better, the editor has summarized the following content for you. I hope you can get something according to this article.

Description of LBP features

The original LBP operator is defined as comparing the gray value of the adjacent 8 pixels with the threshold value of the central pixel in the window of 3: 3. If the value of the surrounding pixel is greater than the value of the central pixel, the position of the pixel is marked as 1, otherwise it is 0. In this way, 8 points in the neighborhood of 3 ~ 3 can produce 8-bit binary numbers (usually converted to decimal numbers, that is, LBP codes, a total of 256), that is, the LBP value of the central pixel of the window, and this value is used to reflect the texture information of the region. The details are as follows:

Improvement of LBP

After reading the basic LBP operator, some readers may wonder, what if an area is circular or other size? The researchers proposed the following improved versions.

(1) Circular LBP operator

In order to adapt to the texture features of different scales and meet the requirements of grayscale and rotation invariance, Ojala et al improved the LBP operator, extending the 3 × 3 neighborhood to any neighborhood and replacing the square with a circular neighborhood.

The improved LBP operator allows for any number of pixels in a circular neighborhood with a radius of R. Thus, a LBP operator with P sampling points in a circular region with radius R is obtained, which is shown as follows:

(2) the equivalent mode of LBP

‍‍ a LBP operator can produce different binary patterns, and the LBP operator with P sampling points in a circular region with radius R will produce a P-power pattern of 2. Obviously, with the increase of the number of sampling points in the neighborhood set, the types of binary patterns increase rapidly. For example: 20 sampling points in 5 × 5 neighborhood, there are 20 power of 2 = 1048576 binary modes. So many binary patterns are disadvantageous not only for texture extraction, but also for texture recognition, classification and information access. At the same time, too many kinds of patterns are disadvantageous to the expression of texture. For example, when LBP operator is used in texture classification or face recognition, the statistical histogram of LBP pattern is often used to express image information, and more patterns will make the amount of data too large and the histogram too sparse. Therefore, it is necessary to reduce the dimension of the original LBP mode, so that the information of the image can be best represented when the amount of data is reduced.

In order to solve the problem of too many binary patterns and improve the statistics, Ojala proposed an "equivalent pattern" (Uniform Pattern) to reduce the dimension of the pattern types of LBP operators. Ojala et al believe that in real images, most LBP modes contain at most two jumps from 1 to 0 or from 0 to 1. Therefore, Ojala defines the "equivalent pattern" as: when the cyclic binary number corresponding to a LBP jumps from 0 to 1 or from 1 to 0 at most, the binary corresponding to the LBP is called an equivalent pattern class. For example, 00000000 (0 jumps), 00000111 (including only one jump from 0 to 1), and 10001111 (jumping from 1 to 0, then from 0 to 1, for a total of two jumps) are all equivalent pattern classes. All patterns except the equivalent pattern class fall into another category, called the mixed pattern class, such as 10010111 (a total of four jumps) (this is my personal understanding, and I don't know if it's right).

With this improvement, the number of binary patterns is greatly reduced without losing any information. The number of patterns is reduced from the P power of 2 to P (Pmur1) + 2, where P represents the number of sampling points in the neighborhood set. For the 8 sampling points in the 3 × 3 neighborhood, the number of binary patterns is reduced from 256 to 58, which reduces the dimension of Eigenvectors and reduces the impact of high-frequency noise.

The use of LBP

In the application of LBP, such as texture classification and face analysis, LBP atlas is generally not used as feature vector for classification and recognition, but the statistical histogram of LBP feature spectrum is used as feature vector for classification and recognition. Because this kind of "feature" is extracted directly from the two images and discriminant analysis is carried out, it will cause a great error because of the "misalignment". Later, the researchers found that a picture can be divided into several sub-regions, LBP features are extracted from each pixel in each sub-region, and then the statistical histogram of LBP features is established in each sub-region. In this way, each sub-region can be described by a statistical histogram; the whole picture consists of several statistical histograms; for example, a picture with a size of 100 pixels is divided into 100 sub-regions (regions can be divided in a variety of ways), each with a size of 10-10 pixels. In each sub-region of each pixel, extract its LBP features, and then establish a statistical histogram; in this way, the picture has 10-10 sub-regions, there are 10-10 statistical histograms, using these 10-10 statistical histograms, the picture can be described. After that, we can judge the similarity between the two images by using various similarity measurement functions. The figure is as follows:

After reading the above, do you have any further understanding of the principle of LBP? If you want to know more knowledge or related content, please follow the industry information channel, thank you for your support.

Welcome to subscribe "Shulou Technology Information " to get latest news, interesting things and hot topics in the IT industry, and controls the hottest and latest Internet news, technology news and IT industry trends.