Local Binary Patterns (LBP) for Texture Analysis
Local Binary Patterns (LBP) is a powerful technique used in image processing and computer vision for texture analysis. It is particularly useful in face recognition tasks by extracting texture features that are invariant to monotonic gray-scale changes. This makes LBP a robust method for recognizing faces under varying illumination conditions.
1. Introduction to LBP
LBP is a simple yet efficient texture descriptor that encodes the local structure of an image. The basic idea behind LBP is to threshold the neighborhood of each pixel and convert the result into a binary number. The binary number is then used as a label for that pixel.1.1 How LBP Works
The LBP operator works by comparing each pixel with its surrounding pixels. Here’s how it’s typically computed: - For a given pixel at position (x, y), consider its 3x3 neighborhood. - For each neighboring pixel (P), compare its intensity to the center pixel (C). If P >= C, assign a value of 1; otherwise, assign 0. - This results in a binary number that can be converted into a decimal value. - The resulting value is then used as the LBP code for the center pixel.1.2 Example Calculation
Let’s take a sample 3x3 pixel neighborhood:`
[ 5, 6, 7 ]
[ 2, 8, 1 ]
[ 4, 3, 0 ]
`
If we consider the center pixel (8):
- Compare each pixel:
- 5 < 8 -> 0
- 6 < 8 -> 0
- 7 < 8 -> 0
- 2 < 8 -> 0
- 8 = 8 -> 0
- 1 < 8 -> 0
- 4 < 8 -> 0
- 3 < 8 -> 0This results in the binary pattern 00000000, which converts to decimal 0, making the LBP code for that pixel equal to 0.
2. LBP Variants
While the basic LBP is effective, several variants have been developed to enhance its performance: - Uniform LBP: This variant considers only the patterns with at most two 1s or 0s in a binary string (e.g., 00000000, 00000001, 00000010, 00000011). All other patterns are grouped into a single bin. - Rotation Invariant LBP: This variant allows for rotation of the neighborhood pixels, making the LBP descriptor invariant to rotation. - Extended LBP: This method uses a larger neighborhood, which captures more texture information, improving the descriptor's robustness.3. LBP in Practice
LBP can be implemented in Python using the OpenCV library. Below is a simple implementation of LBP:`python
import cv2
import numpy as npdef local_binary_pattern(image, P, R):
Compute LBP
lbp = np.zeros_like(image) rows, cols = image.shape for i in range(1, rows-1): for j in range(1, cols-1): center = image[i, j] binary_string = '' for p in range(P): theta = 2 np.pi p / P x = int(R * np.cos(theta)) + j y = int(R * np.sin(theta)) + i binary_string += '1' if image[y, x] >= center else '0' lbp[i, j] = int(binary_string, 2) return lbpExample usage
image = cv2.imread('face.jpg', cv2.IMREAD_GRAYSCALE) lbp_image = local_binary_pattern(image, 8, 1) cv2.imshow('LBP Image', lbp_image) cv2.waitKey(0) cv2.destroyAllWindows()`