In-depth analysis of local binary pattern LBP

Local Binary Pattern (LBP) is a commonly used texture feature descriptor used to reveal the texture information of images. The LBP algorithm was first proposed by Ojala et al. in 1996, and has been continuously improved and developed in subsequent research.

The basic idea of ​​the LBP algorithm is to compare the gray value of each pixel with its surrounding pixels and convert the comparison result into a binary number. In this way, each pixel can be represented as a local binary pattern. By counting the occurrence times of different local binary patterns in the image, the feature vector describing the texture information of the image can be obtained.

The specific implementation steps of the LBP algorithm are as follows:

Select a pixel in the image (called the central pixel) and several pixels around it (called neighbor pixels).

For each neighbor pixel, compare its gray value difference with the center pixel. If the gray value of the neighbor pixel is greater than the central pixel, set it to 1, otherwise set it to 0.

The binary values ​​of neighboring pixels are concatenated to form the local binary pattern of the central pixel.

Traverse the entire image, perform the above operations on each pixel, and finally obtain an image composed of local binary patterns.

For the entire image, count the number of occurrences of different local binary patterns and form a feature vector.

The advantages of the LBP algorithm are that it is simple to calculate, does not require training, and can effectively describe image texture information. Therefore, it is widely used in image recognition, face recognition, pedestrian detection and other fields.

Local binary pattern image feature extraction steps

The local binary pattern algorithm can well describe the texture information of the image, so it is widely used for image feature extraction. The following are the general local binary pattern image feature extraction steps:

1. Image preprocessing: Convert the image into a grayscale image, and perform preprocessing operations such as histogram equalization to improve the contrast and Feature robustness.

2. Select the sampling point and sampling radius: In order to calculate the local binary pattern, you need to select the sampling point and sampling radius. The sampling point is the neighbor pixels around the central pixel, and the sampling radius is the distance from the central pixel to the sampling point.

3. Calculate the local binary pattern: For each pixel in the image, calculate its local binary pattern. Specifically, for each pixel, its gray value is compared with the gray value of the neighbor pixels around it. If the gray value of the neighbor pixel is greater than the gray value of the central pixel, then the weight of the neighbor pixel is is 1, otherwise it is 0. By combining the weights of all neighboring pixels into a binary number, the local binary pattern of the pixel is obtained.

4. Statistics of local binary patterns: For the entire image, count the number of occurrences of different local binary patterns and form a feature vector.

5. Feature vector normalization: Normalize the feature vectors to eliminate scale differences between different images.

6. Feature selection: Perform feature selection on the feature vector and select features that contribute more to the classification effect.

7. Feature classification: Use a classifier to classify feature vectors.

The local binary pattern image feature extraction algorithm is simple and effective, and can well describe the texture information of the image. Therefore, it has been widely used in image classification, face recognition, pedestrian detection and other fields.

How to use local binary pattern for face recognition

The local binary pattern algorithm has been widely used in the field of face recognition. The following are the general face recognition steps based on the LBP algorithm:

1. Data set preparation: Prepare a training set and a test set containing face images. Each image needs to be labeled with the face position and face label. .

2. Image preprocessing: Convert the image into a grayscale image, and perform preprocessing operations such as histogram equalization to improve the contrast of the image and the robustness of the features.

3. Face detection: Use a face detection algorithm (such as the Viola-Jones algorithm) to detect the face in the image, and crop and standardize the face part.

4. Feature extraction: For the cropped and standardized face images, use the LBP algorithm to extract features. The local binary pattern of each pixel is composed into a feature vector, and the feature vector is normalized.

5. Feature dimensionality reduction: Perform dimensionality reduction operations such as PCA or LDA on the feature vector to reduce the dimension of the feature vector and improve the separability of the feature.

6. Classifier training: Use the training set to train the classifier (such as SVM, KNN, etc.).

7. Test set classification: Use the trained classifier to classify the test set and obtain the prediction results.

8. Evaluate the model: Use accuracy, recall, F1 value and other indicators to evaluate the model, and tune the model parameters.

The above steps are the general face recognition process based on the LBP algorithm. In practical applications, it can be adjusted and optimized according to specific problems.

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