LBP (Local Binary Patterns) in computer vision algorithms
Hello everyone, today I want to share with you LBP (Local Binary Patterns) in computer vision algorithms. LBP is a feature extraction algorithm used for image analysis and pattern recognition. It was first proposed by T. Ojala et al. in 1994, and has been continuously developed and improved in subsequent studies. The core idea of the LBP algorithm is to obtain the texture features of the image by comparing and encoding the pixel values of the local area in the image. The specific steps of the LBP algorithm are as follows:
- Select a pixel as the center point, and then select an area with a certain radius, usually an 8-neighborhood or a 16-neighborhood.
- Compare the pixel values within the field to the pixel value at the center point. If the pixel value in the neighborhood is greater than or equal to the pixel value of the center point, the comparison result is set to 1, otherwise it is set to 0.
- Concatenate the comparison results in clockwise or counterclockwise order to form a binary number.
- Convert the obtained binary number to a decimal number to obtain the LBP value of the pixel. By performing LBP calculation on each pixel in the image, we can get the LBP feature map of the entire image. LBP feature maps can be used for tasks such as image classification, object detection, and face recognition. The advantages of the LBP algorithm are simple calculation, strong feature expression ability, and certain robustness to illumination changes and noise. It can capture the texture information in the image, and for images with rich textures, the LBP algorithm performs well.
However, the LBP algorithm also has some shortcomings. First of all, the LBP algorithm only considers the comparison of pixel value sizes, but does not consider the spatial relationship between pixels. Secondly, the LBP algorithm is not invariant to rotation and scale changes in the image. These problems limit the application of the LBP algorithm in certain specific scenarios.
Below is a simple example code for calculating the LBP features of an image:
pythonCopy codeimport cv2
import numpy as np
def get_lbp_feature(image, radius=1, neighbors=8):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
lbp = np.zeros_like(gray, dtype=np.uint8)
for i in range(radius, gray.shape[0] - radius):
for j in range(radius, gray.shape[1] - radius):
center = gray[i, j]
code = 0
for k in range(neighbors):
x = i + int(radius * np.cos(2 * np.pi * k / neighbors))
y = j - int(radius * np.sin(2 * np.pi * k / neighbors))
code <<= 1
if gray[x, y] >= center:
code |= 1
lbp[i, j] = code
hist = np.histogram(lbp, bins=range(256))[0]
hist = hist / np.sum(hist) # 归一化直方图
return hist
# 读取图像
image = cv2.imread('image.jpg')
# 计算LBP特征
hist = get_lbp_feature(image)
# 打印LBP特征直方图
print(hist)In this sample code, we have used the OpenCV library to read and grayscale the image. Then, we define a get_lbp_featurefunction to compute the LBP features of an image. The parameters of the function radiusand neighborsrepresent the radius and the number of neighbors in the LBP algorithm, and the default values are 1 and 8, respectively. The function returns a histogram of LBP features. Finally, we read the image and call get_lbp_featurethe function to calculate the LBP features and print out the feature histogram. Please note that this is just a simple sample code, which may need to be adjusted and optimized according to specific situations in actual applications. Meanwhile, in order to run the sample code, you need to install the OpenCV library and prepare an image file as input.
Here is another example code, also used to calculate LBP features of an image:
pythonCopy codeimport cv2
import numpy as np
def calculate_lbp(image):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
height, width = gray.shape
lbp_image = np.zeros_like(gray)
for i in range(1, height-1):
for j in range(1, width-1):
center = gray[i, j]
code = 0
if gray[i-1, j-1] >= center:
code |= 1
if gray[i-1, j] >= center:
code |= 2
if gray[i-1, j+1] >= center:
code |= 4
if gray[i, j+1] >= center:
code |= 8
if gray[i+1, j+1] >= center:
code |= 16
if gray[i+1, j] >= center:
code |= 32
if gray[i+1, j-1] >= center:
code |= 64
if gray[i, j-1] >= center:
code |= 128
lbp_image[i, j] = code
hist = cv2.calcHist([lbp_image], [0], None, [256], [0,256])
hist /= np.sum(hist)
return hist.flatten()
# 读取图像
image = cv2.imread('image.jpg')
# 计算LBP特征
lbp_feature = calculate_lbp(image)
# 打印LBP特征
print(lbp_feature)In this sample code, we also use the OpenCV library to read the image. Then, we define a calculate_lbpfunction to calculate the LBP features of the image. In the function, an 8-bit binary number is generated as an LBP code by comparing the 8 pixel values around each pixel with the central pixel value. Finally, we use cv2.calcHistthe function to calculate the histogram of the LBP image and normalize it. The function returns a one-dimensional array representing LBP features. Finally, we read the image and call calculate_lbpthe function to calculate the LBP features and print the features. Please note that this is just a simple sample code, and actual application may need to be adjusted and optimized according to specific circumstances. Also, in order to run the sample code, you need to install the OpenCV library and prepare an image file as input.
In order to overcome the limitations of the LBP algorithm, researchers have proposed many improved LBP algorithms, such as rotation-invariant LBP (RILBP), rotation-invariant extended LBP (RIELBP), etc. These improved algorithms have achieved certain results in improving the performance and robustness of the LBP algorithm. In summary, the LBP algorithm is a commonly used image texture feature extraction algorithm in computer vision. Although it has some limitations, by improving and combining with other algorithms, the LBP algorithm still has broad application prospects in the fields of image analysis and pattern recognition. I hope that today's sharing can give you a preliminary understanding of the LBP algorithm. thank you all!